mindspore2022/mindspore/dataset/engine/datasets.py

# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
This dataset module supports various formats of datasets, including ImageNet, TFData,
MNIST, Cifar10/100, Manifest, MindRecord, and more. This module loads data with
high performance and parses data precisely. Some of the operations that are
provided to users to preprocess data include shuffle, batch, repeat, map, and zip.
"""
import glob
import json
import math
import os
import uuid
import multiprocessing
import queue
from enum import Enum
from importlib import import_module
import threading

import copy
import numpy as np

from mindspore._c_dataengine import DataType, TFReaderOp, ImageFolderOp, CifarOp, MnistOp, ManifestOp, \
    MindRecordOp, TextFileOp, ClueOp, CsvOp, VOCOp, CocoOp, CBatchInfo
from mindspore._c_expression import typing

from mindspore import log as logger
from mindspore.parallel._ps_context import _is_role_pserver, _is_role_sched

import mindspore.dataset.transforms.py_transforms as py_transforms

from . import samplers
from .iterators import DictIterator, TupleIterator, DummyIterator, SaveOp, Iterator, check_iterator_cleanup
from .validators import check_batch, check_shuffle, check_map, check_filter, check_repeat, check_skip, check_zip, \
    check_rename, check_numpyslicesdataset, check_device_send, \
    check_take, check_project, check_imagefolderdataset, check_mnist_cifar_dataset, check_manifestdataset, \
    check_tfrecorddataset, check_vocdataset, check_cocodataset, check_celebadataset, check_minddataset, \
    check_generatordataset, check_sync_wait, check_zip_dataset, check_add_column, check_textfiledataset, check_concat, \
    check_random_dataset, check_split, check_bucket_batch_by_length, check_cluedataset, check_save, check_csvdataset, \
    check_paddeddataset, check_iterator
from ..core.config import get_callback_timeout
from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
from ..text.utils import DE_C_INTER_SENTENCEPIECE_MODE

try:
    context = import_module("mindspore.context")
except ModuleNotFoundError:
    context = None


class Shuffle(str, Enum):
    GLOBAL: str = "global"
    FILES: str = "file"


@check_zip
def zip(datasets):
    """
    Zip the datasets in the input tuple of datasets.

    Args:
        datasets (tuple of class Dataset): A tuple of datasets to be zipped together.
            The number of datasets must be more than 1.

    Returns:
        DatasetOp, ZipDataset.

    Raises:
        ValueError: If the number of datasets is 1.
        TypeError: If datasets is not a tuple.

    Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> dataset_dir1 = "path/to/imagefolder_directory1"
            >>> dataset_dir2 = "path/to/imagefolder_directory2"
            >>> ds1 = ds.ImageFolderDataset(dataset_dir1, num_parallel_workers=8)
            >>> ds2 = ds.ImageFolderDataset(dataset_dir2, num_parallel_workers=8)
            >>>
            >>> # Create a dataset which is the combination of ds1 and ds2
            >>> data = ds.zip((ds1, ds2))
    """
    if len(datasets) <= 1:
        raise ValueError(
            "Can't zip empty or just one dataset!")
    return ZipDataset(datasets)


def get_num_rows(num_rows, num_shards):
    """
    Get the number rows of the dataset according to the shards.

    Args:
        num_rows (int): Number rows of the dataset. It must be more than 0.
        num_shards (int or None): Number of shards that the dataset will be divided into.
            The number of shards must be None or more than 1.

    Returns:
        Int, number of rows.

    Raises:
        ValueError: If num_rows is invalid (< 0).
        ValueError: If num_shards is invalid (<= 0).
    """
    if num_rows < 0:
        raise ValueError("num_rows is invalid (< 0)")

    if num_shards is not None:
        if num_shards <= 0:
            raise ValueError("num_shards is invalid (<= 0)")
        if num_rows % num_shards == 0:
            num_rows = num_rows // num_shards
        else:
            num_rows = num_rows // num_shards + 1
    return num_rows


class Dataset:
    """
    Abstract class to represent a dataset in DataEngine's data pipeline.

    This class is the base class of SourceDataset and DatasetOp, and represents
    a node in the data flow graph.

    Args:
        num_parallel_workers (int, optional): Number of workers to process the dataset in parallel
            (default=None).
    """

    def __init__(self, num_parallel_workers=None):
        # Note: children and parent are internal variables, not recommand for external using.
        self.children = []
        self.parent = []
        self.num_parallel_workers = num_parallel_workers
        self._device_iter = 0
        self._input_indexs = ()
        self._output_types = None
        self._output_shapes = None
        self.dataset_size = None
        self._batch_size = None
        self._num_classes = None
        self._repeat_count = None
        self._sync = False

    def _noop_mode(self):
        if _is_role_sched() or _is_role_pserver():
            return True
        return False

    def __add__(self, datasets):
        return self.concat(datasets)

    def get_args(self):
        """
        Return attributes (member variables) related to the current class.

        Must include all arguments passed to the __init__() of the current class, excluding 'input_dataset'.

        Args:

        Returns:
            Python dictionary.
        """
        args = dict()
        args["num_parallel_workers"] = self.num_parallel_workers
        return args

    @check_bucket_batch_by_length
    def bucket_batch_by_length(self, column_names, bucket_boundaries, bucket_batch_sizes,
                               element_length_function=None, pad_info=None,
                               pad_to_bucket_boundary=False, drop_remainder=False):
        """
        Bucket elements according to their lengths. Each bucket will be padded and batched when
        they are full.

        A length function is called on each row in the dataset. The row is then
        bucketed based on its length and bucket_boundaries. When a bucket reaches its
        corresponding size specified in bucket_batch_sizes, the entire bucket will be
        padded according to batch_info, and then batched. Each batch will be full,
        except for maybe the last batch for each bucket.

        Args:
            column_names (list[str]): Columns passed to element_length_function.
            bucket_boundaries (list[int]): A list consisting of the upper boundaries
                of the buckets. Must be strictly increasing. If there are n boundaries,
                n+1 buckets are created: One bucket for [0, bucket_boundaries[0]), one
                bucket for [bucket_boundaries[i], bucket_boundaries[i+1]) for each
                0<i<n, and one bucket for [bucket_boundaries[n-1], inf).
            bucket_batch_sizes (list[int]): A list consisting of the batch sizes for
                each bucket. Must contain len(bucket_boundaries)+1 elements.
            element_length_function (Callable, optional): A function that takes in
                len(column_names) arguments and returns an int. If no value is
                provided, then len(column_names) must be 1, and the size of the first
                dimension of that column will be taken as the length (default=None).
            pad_info (dict, optional): Represents how to batch each column. The key
                corresponds to the column name, and the value must be a tuple of 2 elements.
                The first element corresponds to the shape to pad to, and the second
                element corresponds to the value to pad with. If a column is not
                specified, then that column will be padded to the longest in the current
                batch, and 0 will be used as the padding value. Any None dimensions will
                be padded to the longest in the current batch, unless if
                pad_to_bucket_boundary is True. If no padding is wanted, set pad_info
                to None (default=None).
            pad_to_bucket_boundary (bool, optional): If True, will pad each None
                dimension in pad_info to the bucket_boundary minus 1. If there are any
                elements that fall into the last bucket, an error will occur
                (default=False).
            drop_remainder (bool, optional): If True, will drop the last batch for each
                bucket if it is not a full batch (default=False).

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>>
            >>> # Create a dataset where every 100 rows is combined into a batch
            >>> # and drops the last incomplete batch if there is one.
            >>> column_names = ["col1", "col2"]
            >>> buket_boundaries = [5, 10]
            >>> bucket_batch_sizes = [5, 1, 1]
            >>> element_length_function = (lambda col1, col2: max(len(col1), len(col2)))
            >>>
            >>> # Will pad col1 to shape [2, bucket_boundaries[i]] where i is the
            >>> # index of the bucket that is currently being batched.
            >>> # Will pad col2 to a shape where each dimension is the longest in all
            >>> # the elements currently being batched.
            >>> pad_info = {"col1", ([2, None], -1)}
            >>> pad_to_bucket_boundary = True
            >>>
            >>> data = data.bucket_batch_by_length(column_names, bucket_boundaries,
            >>>                                    bucket_batch_sizes,
            >>>                                    element_length_function, pad_info,
            >>>                                    pad_to_bucket_boundary)
        """
        return BucketBatchByLengthDataset(self, column_names, bucket_boundaries, bucket_batch_sizes,
                                          element_length_function, pad_info,
                                          pad_to_bucket_boundary, drop_remainder)

    @check_batch
    def batch(self, batch_size, drop_remainder=False, num_parallel_workers=None, per_batch_map=None,
              input_columns=None, output_columns=None, column_order=None, pad_info=None):
        """
        Combine batch_size number of consecutive rows into batches.

        For any child node, a batch is treated as a single row.
        For any column, all the elements within that column must have the same shape.
        If a per_batch_map callable is provided, it will be applied to the batches of tensors.

        Note:
            The order of using repeat and batch reflects the number of batches and per_batch_map.
            It is recommended that the repeat operation be used after the batch operation.

        Args:
            batch_size (int or function): The number of rows each batch is created with. An
                int or callable which takes exactly 1 parameter, BatchInfo.
            drop_remainder (bool, optional): Determines whether or not to drop the last
                possibly incomplete batch (default=False). If True, and if there are less
                than batch_size rows available to make the last batch, then those rows will
                be dropped and not propagated to the child node.
            num_parallel_workers (int, optional): Number of workers to process the dataset in parallel (default=None).
            per_batch_map (callable, optional): Per batch map callable. A callable which takes
                (list[Tensor], list[Tensor], ..., BatchInfo) as input parameters. Each list[Tensor] represents a batch
                of Tensors on a given column. The number of lists should match with number of entries in input_columns.
                The last parameter of the callable should always be a BatchInfo object.
            input_columns (list[str], optional): List of names of the input columns. The size of the list should
                match with signature of per_batch_map callable.
            output_columns (list[str], optional): List of names assigned to the columns
                outputted by the last operation. This parameter is mandatory if len(input_columns) !=
                len(output_columns). The size of this list must match the number of output
                columns of the last operation. (default=None, output columns will have the same
                name as the input columns, i.e., the columns will be replaced).
            column_order (list[str], optional): List of all the desired columns to propagate to
                the child node. This list must be a subset of all the columns in the dataset after
                all operations are applied. The order of the columns in each row propagated to the
                child node follow the order they appear in this list. The parameter is mandatory
                if the len(input_columns) != len(output_columns). (default=None, all columns
                will be propagated to the child node, the order of the columns will remain the
                same).
            pad_info (dict, optional): Whether to perform padding on selected columns. pad_info={"col1":([224,224],0)}
                would pad column with name "col1" to a tensor of size [224,224] and fill the missing with 0.

        Returns:
            BatchDataset, dataset batched.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>>
            >>> # Create a dataset where every 100 rows is combined into a batch
            >>> # and drops the last incomplete batch if there is one.
            >>> data = data.batch(100, True)
            >>>
            >>> # resize image according to its batch number, if it's 5-th batch, resize to (5^2, 5^2) = (25, 25)
            >>> def np_resize(col, batchInfo):
            >>>     output = col.copy()
            >>>     s = (batchInfo.get_batch_num() + 1) ** 2
            >>>     index = 0
            >>>     for c in col:
            >>>         img = Image.fromarray(c.astype('uint8')).convert('RGB')
            >>>         img = img.resize((s, s), Image.ANTIALIAS)
            >>>         output[index] = np.array(img)
            >>>         index += 1
            >>>     return (output,)
            >>> data = data.batch(batch_size=8, input_columns=["image"], per_batch_map=np_resize)
        """
        return BatchDataset(self, batch_size, drop_remainder, num_parallel_workers, per_batch_map, input_columns,
                            output_columns, column_order, pad_info)

    @check_sync_wait
    def sync_wait(self, condition_name, num_batch=1, callback=None):
        '''
        Add a blocking condition to the input Dataset.

        Args:
            num_batch (int): the number of batches without blocking at the start of each epoch.
            condition_name (str): The condition name that is used to toggle sending next row.
            callback (function): The callback funciton that will be invoked when sync_update is called.

        Raises:
            RuntimeError: If condition name already exists.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>> data = data.sync_wait("callback1")
            >>> data = data.batch(batch_size)
            >>> for batch_data in data.create_dict_iterator():
            >>>     data = data.sync_update("callback1")
        '''
        return SyncWaitDataset(self, condition_name, num_batch, callback)

    @check_shuffle
    def shuffle(self, buffer_size):
        """
        Randomly shuffles the rows of this dataset using the following algorithm:

        1. Make a shuffle buffer that contains the first buffer_size rows.
        2. Randomly select an element from the shuffle buffer to be the next row
           propogated to the child node.
        3. Get the next row (if any) from the parent node and put it in the shuffle buffer.
        4. Repeat steps 2 and 3 until there are no more rows left in the shuffle buffer.

        A seed can be provided to be used on the first epoch. In every subsequent
        epoch, the seed is changed to a new one, randomly generated value.

        Args:
            buffer_size (int): The size of the buffer (must be larger than 1) for
                shuffling. Setting buffer_size equal to the number of rows in the entire
                dataset will result in a global shuffle.

        Returns:
            ShuffleDataset, dataset shuffled.

        Raises:
            RuntimeError: If exist sync operators before shuffle.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>> # Optionally set the seed for the first epoch
            >>> ds.config.set_seed(58)
            >>>
            >>> # Create a shuffled dataset using a shuffle buffer of size 4
            >>> data = data.shuffle(4)
        """
        return ShuffleDataset(self, buffer_size)

    def flat_map(self, func):
        """
        Map `func` to each row in dataset and flatten the result.

        The specified `func` is a function that must take one 'Ndarray' as input
        and return a 'Dataset'.

        Args:
            func (function): A function that must take one 'Ndarray' as an argument and
                return a 'Dataset'.

        Returns:
            Dataset, applied by the function.

        Examples:
            >>> import mindspore.dataset as ds
            >>> import mindspore.dataset.text as text
            >>>
            >>> # Declare a function which returns a Dataset object
            >>> def flat_map_func(x):
            >>>     data_dir = text.to_str(x[0])
            >>>     d = ds.ImageFolderDataset(data_dir)
            >>>     return d
            >>> # data is an instance of a Dataset object.
            >>> data = ds.TextFileDataset(DATA_FILE)
            >>> data = data.flat_map(flat_map_func)

        Raises:
            TypeError: If `func` is not a function.
            TypeError: If `func` doesn't return a Dataset.
        """
        dataset = None
        if not hasattr(func, '__call__'):
            logger.error("func must be a function.")
            raise TypeError("func must be a function.")

        for row_data in self.create_tuple_iterator(output_numpy=True):
            if dataset is None:
                dataset = func(row_data)
            else:
                dataset += func(row_data)

        if not isinstance(dataset, Dataset):
            logger.error("flat_map must return a Dataset object.")
            raise TypeError("flat_map must return a Dataset object.")
        return dataset

    @check_map
    def map(self, operations, input_columns=None, output_columns=None, column_order=None,
            num_parallel_workers=None, python_multiprocessing=False, cache=None, callbacks=None):
        """
        Apply each operation in operations to this dataset.

        The order of operations is determined by the position of each operation in the operations parameter.
        operations[0] will be applied first, then operations[1], then operations[2], etc.

        Each operation will be passed one or more columns from the dataset as input, and zero or
        more columns will be outputted. The first operation will be passed the columns specified
        in input_columns as input. If there is more than one operator in operations, the outputted
        columns of the previous operation are used as the input columns for the next operation.
        The columns outputted by the very last operation will be assigned names specified by
        output_columns.

        Only the columns specified in column_order will be propagated to the child node. These
        columns will be in the same order as specified in column_order.

        Args:
            operations (Union[list[TensorOp], list[functions]]): List of operations to be
                applied on the dataset. Operations are applied in the order they appear in this list.
            input_columns (list[str], optional): List of the names of the columns that will be passed to
                the first operation as input. The size of this list must match the number of
                input columns expected by the first operator. (default=None, the first
                operation will be passed however many columns that is required, starting from
                the first column).
            output_columns (list[str], optional): List of names assigned to the columns outputted by
                the last operation. This parameter is mandatory if len(input_columns) !=
                len(output_columns). The size of this list must match the number of output
                columns of the last operation. (default=None, output columns will have the same
                name as the input columns, i.e., the columns will be replaced).
            column_order (list[str], optional): List of all the desired columns to propagate to the
                child node. This list must be a subset of all the columns in the dataset after
                all operations are applied. The order of the columns in each row propagated to the
                child node follow the order they appear in this list. The parameter is mandatory
                if the len(input_columns) != len(output_columns). (default=None, all columns
                will be propagated to the child node, the order of the columns will remain the
                same).
            num_parallel_workers (int, optional): Number of threads used to process the dataset in
                parallel (default=None, the value from the configuration will be used).
            python_multiprocessing (bool, optional): Parallelize Python operations with multiple worker processes. This
                option could be beneficial if the Python operation is computational heavy (default=False).
            cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
                The cache feature is under development and is not recommended.
            callbacks: (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None).


        Returns:
            MapDataset, dataset after mapping operation.

        Examples:
            >>> import mindspore.dataset as ds
            >>> import mindspore.dataset.vision.c_transforms as c_transforms
            >>>
            >>> # data is an instance of Dataset which has 2 columns, "image" and "label".
            >>> # ds_pyfunc is an instance of Dataset which has 3 columns, "col0", "col1", and "col2".
            >>> # Each column is a 2D array of integers.
            >>>
            >>> # Set the global configuration value for num_parallel_workers to be 2.
            >>> # Operations which use this configuration value will use 2 worker threads,
            >>> # unless otherwise specified in the operator's constructor.
            >>> # set_num_parallel_workers can be called again later if a different
            >>> # global configuration value for the number of worker threads is desired.
            >>> ds.config.set_num_parallel_workers(2)
            >>>
            >>> # Define two operations, where each operation accepts 1 input column and outputs 1 column.
            >>> decode_op = c_transforms.Decode(rgb_format=True)
            >>> random_jitter_op = c_transforms.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0))
            >>>
            >>> # 1) Simple map example
            >>>
            >>> operations = [decode_op]
            >>> input_columns = ["image"]
            >>>
            >>> # Apply decode_op on column "image". This column will be replaced by the outputted
            >>> # column of decode_op. Since column_order is not provided, both columns "image"
            >>> # and "label" will be propagated to the child node in their original order.
            >>> ds_decoded = data.map(operations, input_columns)
            >>>
            >>> # Rename column "image" to "decoded_image".
            >>> output_columns = ["decoded_image"]
            >>> ds_decoded = data.map(operations, input_columns, output_columns)
            >>>
            >>> # Specify the order of the columns.
            >>> column_order ["label", "image"]
            >>> ds_decoded = data.map(operations, input_columns, None, column_order)
            >>>
            >>> # Rename column "image" to "decoded_image" and also specify the order of the columns.
            >>> column_order ["label", "decoded_image"]
            >>> output_columns = ["decoded_image"]
            >>> ds_decoded = data.map(operations, input_columns, output_columns, column_order)
            >>>
            >>> # Rename column "image" to "decoded_image" and keep only this column.
            >>> column_order ["decoded_image"]
            >>> output_columns = ["decoded_image"]
            >>> ds_decoded = data.map(operations, input_columns, output_columns, column_order)
            >>>
            >>> # A simple example using pyfunc: Renaming columns and specifying column order
            >>> # work in the same way as the previous examples.
            >>> input_columns = ["col0"]
            >>> operations = [(lambda x: x + 1)]
            >>> ds_mapped = ds_pyfunc.map(operations, input_columns)
            >>>
            >>> # 2) Map example with more than one operation
            >>>
            >>> # If this list of operations is used with map, decode_op will be applied
            >>> # first, then random_jitter_op will be applied.
            >>> operations = [decode_op, random_jitter_op]
            >>>
            >>> input_columns = ["image"]
            >>>
            >>> # Create a dataset where the images are decoded, then randomly color jittered.
            >>> # decode_op takes column "image" as input and outputs one column. The column
            >>> # outputted by decode_op is passed as input to random_jitter_op.
            >>> # random_jitter_op will output one column. Column "image" will be replaced by
            >>> # the column outputted by random_jitter_op (the very last operation). All other
            >>> # columns are unchanged. Since column_order is not specified, the order of the
            >>> # columns will remain the same.
            >>> ds_mapped = data.map(operations, input_columns)
            >>>
            >>> # Create a dataset that is identical to ds_mapped, except the column "image"
            >>> # that is outputted by random_jitter_op is renamed to "image_transformed".
            >>> # Specifying column order works in the same way as examples in 1).
            >>> output_columns = ["image_transformed"]
            >>> ds_mapped_and_renamed = data.map(operation, input_columns, output_columns)
            >>>
            >>> # Multiple operations using pyfunc: Renaming columns and specifying column order
            >>> # work in the same way as examples in 1).
            >>> input_columns = ["col0"]
            >>> operations = [(lambda x: x + x), (lambda x: x - 1)]
            >>> output_columns = ["col0_mapped"]
            >>> ds_mapped = ds_pyfunc.map(operations, input_columns, output_columns)
            >>>
            >>> # 3) Example where number of input columns is not equal to number of output columns
            >>>
            >>> # operations[0] is a lambda that takes 2 columns as input and outputs 3 columns.
            >>> # operations[1] is a lambda that takes 3 columns as input and outputs 1 column.
            >>> # operations[1] is a lambda that takes 1 column as input and outputs 4 columns.
            >>> #
            >>> # Note: The number of output columns of operation[i] must equal the number of
            >>> # input columns of operation[i+1]. Otherwise, this map call will also result
            >>> # in an error.
            >>> operations = [(lambda x y: (x, x + y, x + y + 1)),
            >>>               (lambda x y z: x * y * z),
            >>>               (lambda x: (x % 2, x % 3, x % 5, x % 7))]
            >>>
            >>> # Note: Since the number of input columns is not the same as the number of
            >>> # output columns, the output_columns and column_order parameters must be
            >>> # specified. Otherwise, this map call will also result in an error.
            >>> input_columns = ["col2", "col0"]
            >>> output_columns = ["mod2", "mod3", "mod5", "mod7"]
            >>>
            >>> # Propagate all columns to the child node in this order:
            >>> column_order = ["col0", "col2", "mod2", "mod3", "mod5", "mod7", "col1"]
            >>> ds_mapped = ds_pyfunc.map(operations, input_columns, output_columns, column_order)
            >>>
            >>> # Propagate some columns to the child node in this order:
            >>> column_order = ["mod7", "mod3", "col1"]
            >>> ds_mapped = ds_pyfunc.map(operations, input_columns, output_columns, column_order)
        """

        return MapDataset(self, operations, input_columns, output_columns, column_order, num_parallel_workers,
                          python_multiprocessing, cache, callbacks)

    @check_filter
    def filter(self, predicate, input_columns=None, num_parallel_workers=1):
        """
        Filter dataset by predicate.

        Note:
             If input_columns not provided or empty, all columns will be used.

        Args:
            predicate (callable): Python callable which returns a boolean value. If False then filter the element.
            input_columns (list[str], optional): List of names of the input columns, when
                default=None, the predicate will be applied on all columns in the dataset.
            num_parallel_workers (int, optional): Number of workers to process the dataset
                in parallel (default=None).

        Returns:
            FilterDataset, dataset filter.

        Examples:
            >>> import mindspore.dataset as ds
            >>> # generator data(0 ~ 63)
            >>> # filter the data that greater than or equal to 11
            >>> dataset_f = dataset.filter(predicate=lambda data: data < 11, input_columns = ["data"])
        """
        return FilterDataset(self, predicate, input_columns, num_parallel_workers)

    @check_repeat
    def repeat(self, count=None):
        """
        Repeat this dataset count times. Repeat indefinitely if the count is None or -1.

        Note:
            The order of using repeat and batch reflects the number of batches. It is recommended that
            the repeat operation be used after the batch operation.
            If dataset_sink_mode is False, the repeat operation is invalid.
            If dataset_sink_mode is True, repeat count must be equal to the epoch of training. Otherwise,
            errors could occur since the amount of data is not the amount training requires.

        Args:
            count (int): Number of times the dataset is repeated (default=None).

        Returns:
            RepeatDataset, dataset repeated.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>>
            >>> # Create a dataset where the dataset is repeated for 50 epochs
            >>> repeated = data.repeat(50)
            >>>
            >>> # Create a dataset where each epoch is shuffled individually
            >>> shuffled_and_repeated = data.shuffle(10)
            >>> shuffled_and_repeated = shuffled_and_repeated.repeat(50)
            >>>
            >>> # Create a dataset where the dataset is first repeated for
            >>> # 50 epochs before shuffling. The shuffle operator will treat
            >>> # the entire 50 epochs as one big dataset.
            >>> repeat_and_shuffle = data.repeat(50)
            >>> repeat_and_shuffle = repeat_and_shuffle.shuffle(10)
        """
        if count == 1:
            return self
        return RepeatDataset(self, count)

    @check_skip
    def skip(self, count):
        """
        Skip the first N elements of this dataset.

        Args:
            count (int): Number of elements in the dataset to be skipped.

        Returns:
            SkipDataset, dataset skipped.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>> # Create a dataset which skips first 3 elements from data
            >>> data = data.skip(3)
        """
        return SkipDataset(self, count)

    @check_take
    def take(self, count=-1):
        """
        Takes at most given numbers of elements from the dataset.

        Note:
            1. If count is greater than the number of elements in the dataset or equal to -1,
               all the elements in dataset will be taken.
            2. The order of using take and batch matters. If take is before batch operation,
               then take given number of rows; otherwise take given number of batches.

        Args:
            count (int, optional): Number of elements to be taken from the dataset (default=-1).

        Returns:
            TakeDataset, dataset taken.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>> # Create a dataset where the dataset includes 50 elements.
            >>> data = data.take(50)
        """
        if count == -1:
            return self
        return TakeDataset(self, count)

    def _get_absolute_split_sizes(self, sizes):
        """
        Internal method called by split to calculate absolute split sizes and to
        do some error checking after calculating absolute split sizes.
        """
        # Call get_dataset_size here and check input here because
        # don't want to call this once in check_split and another time in
        # here again
        dataset_size = self.get_dataset_size()

        if dataset_size is None or dataset_size <= 0:
            raise RuntimeError("dataset_size is unknown, unable to split.")

        if not isinstance(sizes, list):
            raise RuntimeError("sizes must be a list.")

        all_int = all(isinstance(item, int) for item in sizes)
        if all_int:
            sizes_sum = sum(sizes)
            if sizes_sum != dataset_size:
                raise RuntimeError("Sum of split sizes {} is not equal to dataset size {}."
                                   .format(sizes_sum, dataset_size))
            return sizes

        absolute_sizes = []
        for item in sizes:
            absolute_size = int(round(item * dataset_size))
            if absolute_size == 0:
                raise RuntimeError("Split percentage {} is too small.".format(item))
            absolute_sizes.append(absolute_size)

        absolute_sizes_sum = sum(absolute_sizes)

        # if we still need more rows, give them to the first split.
        # if we have too many rows, remove the extras from the first split that has
        # enough rows.
        size_difference = int(dataset_size - absolute_sizes_sum)
        if size_difference > 0:
            absolute_sizes[0] += size_difference
        else:
            for i, _ in enumerate(absolute_sizes):
                if absolute_sizes[i] + size_difference > 0:
                    absolute_sizes[i] += size_difference
                    break

        if sum(absolute_sizes) != dataset_size:
            raise RuntimeError("Sum of calculated split sizes {} is not equal to dataset size {}."
                               .format(absolute_sizes_sum, dataset_size))

        return absolute_sizes

    @check_split
    def split(self, sizes, randomize=True):
        """
        Split the dataset into smaller, non-overlapping datasets.

        This is a general purpose split function which can be called from any operator in the pipeline.
        There is another, optimized split function, which will be called automatically if ds.split is
        called where ds is a MappableDataset.

        Args:
            sizes (Union[list[int], list[float]]): If a list of integers [s1, s2, …, sn] is
                provided, the dataset will be split into n datasets of size s1, size s2, …, size sn
                respectively. If the sum of all sizes does not equal the original dataset size, an
                error will occur.
                If a list of floats [f1, f2, …, fn] is provided, all floats must be between 0 and 1
                and must sum to 1, otherwise an error will occur. The dataset will be split into n
                Datasets of size round(f1*K), round(f2*K), …, round(fn*K) where K is the size of the
                original dataset.
                If after rounding:

                    - Any size equals 0, an error will occur.

                    - The sum of split sizes < K, the difference will be added to the first split.

                    - The sum of split sizes > K, the difference will be removed from the first large
                      enough split such that it will have at least 1 row after removing the difference.

            randomize (bool, optional): Determines whether or not to split the data randomly (default=True).
                If True, the data will be randomly split. Otherwise, each split will be created with
                consecutive rows from the dataset.

        Note:
            1. Dataset cannot be sharded if split is going to be called.
            2. It is strongly recommended to not shuffle the dataset, but use randomize=True instead.
               Shuffling the dataset may not be deterministic, which means the data in each split
               will be different in each epoch.

        Raises:
            RuntimeError: If get_dataset_size returns None or is not supported for this dataset.
            RuntimeError: If sizes is list of integers and sum of all elements in sizes does not
                equal the dataset size.
            RuntimeError: If sizes is list of float and there is a split with size 0 after calculations.
            RuntimeError: If the dataset is sharded prior to calling split.
            ValueError: If sizes is list of float and not all floats are between 0 and 1, or if the
                floats don’t sum to 1.

        Returns
            tuple(Dataset), a tuple of datasets that have been split.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> dataset_files = "/path/to/text_file/*"
            >>>
            >>> # TextFileDataset is not a mappable dataset, so this non-optimized split will be called.
            >>> # Since many datasets have shuffle on by default, set shuffle to False if split will be called!
            >>> data = ds.TextFileDataset(dataset_files, shuffle=False)
            >>> train, test = data.split([0.9, 0.1])
        """
        if self.is_shuffled():
            logger.warning("Dataset is shuffled before split.")

        if self.is_sharded():
            raise RuntimeError("Dataset should not be sharded before split.")

        absolute_sizes = self._get_absolute_split_sizes(sizes)
        splits = []
        rows_to_skip = 0
        for size in absolute_sizes:
            ds = copy.deepcopy(self)
            if randomize:
                # want to shuffle the same way every epoch before split
                # in alter_tree, shuffle buffer is minimum 10000, so use 10000 here
                ds = ds.shuffle(10000)
                ds.reshuffle_each_epoch = False

            if rows_to_skip > 0:
                ds = ds.skip(rows_to_skip)

            ds = ds.take(size)
            splits.append(ds)

            rows_to_skip += size

        return tuple(splits)

    @check_zip_dataset
    def zip(self, datasets):
        """
        Zip the datasets in the input tuple of datasets. Columns in the input datasets must not have the same name.

        Args:
            datasets (Union[tuple, class Dataset]): A tuple of datasets or a single class Dataset
                to be zipped together with this dataset.

        Returns:
            ZipDataset, dataset zipped.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # ds1 and ds2 are instances of Dataset object
            >>> # Create a dataset which is the combination of ds1 and ds2
            >>> data = ds1.zip(ds2)
        """
        if isinstance(datasets, tuple):
            datasets = (self, *datasets)
        elif isinstance(datasets, Dataset):
            datasets = (self, datasets)
        else:
            raise TypeError("The zip function %s type error!" % (datasets))
        return ZipDataset(datasets)

    @check_concat
    def concat(self, datasets):
        """
        Concatenate the datasets in the input list of datasets. The "+" operator is also supported to concatenate.

        Note:
            The column name, and rank and type of the column data must be the same in the input datasets.

        Args:
            datasets (Union[list, class Dataset]): A list of datasets or a single class Dataset
                to be concatenated together with this dataset.

        Returns:
            ConcatDataset, dataset concatenated.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # ds1 and ds2 are instances of Dataset object
            >>>
            >>> # Create a dataset by concatenating ds1 and ds2 with "+" operator
            >>> data1 = ds1 + ds2
            >>> # Create a dataset by concatenating ds1 and ds2 with concat operation
            >>> data1 = ds1.concat(ds2)
        """
        if isinstance(datasets, Dataset):
            datasets = [self] + [datasets]
        elif isinstance(datasets, list):
            datasets = [self] + datasets
        else:
            raise TypeError("The concat_dataset function %s type error!" % (datasets))
        return ConcatDataset(datasets)

    @check_rename
    def rename(self, input_columns, output_columns):
        """
        Rename the columns in input datasets.

        Args:
            input_columns (list[str]): List of names of the input columns.
            output_columns (list[str]): List of names of the output columns.

        Returns:
            RenameDataset, dataset renamed.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object.
            >>> input_columns = ["input_col1", "input_col2", "input_col3"]
            >>> output_columns = ["output_col1", "output_col2", "output_col3"]
            >>>
            >>> # Create a dataset where input_col1 is renamed to output_col1, and
            >>> # input_col2 is renamed to output_col2, and input_col3 is renamed
            >>> # to output_col3.
            >>> data = data.rename(input_columns=input_columns, output_columns=output_columns)
        """

        return RenameDataset(self, input_columns, output_columns)

    @check_project
    def project(self, columns):
        """
        Project certain columns in input dataset.

        The specified columns will be selected from the dataset and passed down
        the pipeline in the order specified. The other columns are discarded.

        Args:
            columns(list[str]): List of names of the columns to project.

        Returns:
            ProjectDataset, dataset projected.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object
            >>> columns_to_project = ["column3", "column1", "column2"]
            >>>
            >>> # Create a dataset that consists of column3, column1, column2
            >>> # in that order, regardless of the original order of columns.
            >>> data = data.project(columns=columns_to_project)
        """

        return ProjectDataset(self, columns)

    def build_vocab(self, vocab, columns, freq_range, top_k, special_tokens, special_first):
        return BuildVocabDataset(self, vocab, columns, freq_range, top_k, special_tokens, special_first)

    def build_sentencepiece_vocab(self, vocab, col_names, vocab_size,
                                  character_coverage, model_type, params):
        return BuildSentencePieceVocabDataset(self, vocab, col_names, vocab_size, character_coverage,
                                              model_type, params)

    def apply(self, apply_func):
        """
        Apply a function in this dataset.

        Args:
            apply_func (function): A function that must take one 'Dataset' as an argument and
                                   return a preprogressing 'Dataset'.

        Returns:
            Dataset, applied by the function.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object
            >>>
            >>> # Declare an apply_func function which returns a Dataset object
            >>> def apply_func(ds):
            >>>     ds = ds.batch(2)
            >>>     return ds
            >>>
            >>> # Use apply to call apply_func
            >>> data = data.apply(apply_func)

        Raises:
            TypeError: If apply_func is not a function.
            TypeError: If apply_func doesn't return a Dataset.
        """

        if not hasattr(apply_func, '__call__'):
            raise TypeError("apply_func must be a function.")

        dataset = apply_func(self)
        if not isinstance(dataset, Dataset):
            raise TypeError("apply_func must return a dataset.")
        return dataset

    @check_device_send
    def device_que(self, prefetch_size=None, send_epoch_end=True):
        """
        Return a transferred Dataset that transfers data through a device.

        Args:
            prefetch_size (int, optional): Prefetch number of records ahead of the
                user's request (default=None).
            send_epoch_end (bool, optional): Whether to send end of sequence to device or not (default=True).

        Note:
            If device is Ascend, features of data will be transferred one by one. The limitation
            of data transmission per time is 256M.

        Return:
            TransferDataset, dataset for transferring.
        """
        return self.to_device(send_epoch_end=send_epoch_end)

    @check_device_send
    def to_device(self, send_epoch_end=True):
        """
        Transfer data through CPU, GPU or Ascend devices.

        Args:
            send_epoch_end (bool, optional): Whether to send end of sequence to device or not (default=True).

        Note:
            If device is Ascend, features of data will be transferred one by one. The limitation
            of data transmission per time is 256M.

        Returns:
            TransferDataset, dataset for transferring.

        Raises:
            TypeError: If device_type is empty.
            ValueError: If device_type is not 'Ascend', 'GPU' or 'CPU'.
            RuntimeError: If dataset is unknown.
            RuntimeError: If distribution file path is given but failed to read.
        """
        queue_name = str(uuid.uuid1())

        if context:
            device_type = context.get_context("device_target")
        else:
            device_type = "CPU"

        if device_type == "":
            raise TypeError("Please set device_type in context")

        if device_type not in ('Ascend', 'GPU', 'CPU'):
            raise ValueError("Only support CPU, Ascend, GPU")

        def get_distribution(output_dataset):
            dev_id = 0
            if isinstance(output_dataset, (Cifar10Dataset, Cifar100Dataset, GeneratorDataset, ImageFolderDataset,
                                           ManifestDataset, MnistDataset, VOCDataset, CocoDataset, CelebADataset,
                                           MindDataset)):
                sampler = output_dataset.sampler
                if isinstance(sampler, samplers.DistributedSampler):
                    dev_id = sampler.shard_id
                return "", dev_id
            if isinstance(output_dataset, (TFRecordDataset, TextFileDataset, CLUEDataset, CSVDataset)):
                if output_dataset.shard_id is not None:
                    dev_id = output_dataset.shard_id
                return "", dev_id

            if not output_dataset.children:
                raise RuntimeError("Unknown output_dataset: {}".format(type(output_dataset)))
            input_dataset = output_dataset.children[0]
            return get_distribution(input_dataset)

        distribution_path, device_id = get_distribution(self)
        if distribution_path == "":
            return TransferDataset(self, queue_name, device_id, device_type, send_epoch_end)
        try:
            with open(distribution_path, 'r') as distribution_f:
                dist = json.load(distribution_f)
                device_id = dist["deviceId"]
        except json.decoder.JSONDecodeError:
            raise RuntimeError("Json decode error when load distribution file")
        except Exception:
            raise RuntimeError("Distribution file failed to read")

        return TransferDataset(self, queue_name, device_id, device_type, send_epoch_end)

    @check_save
    def save(self, file_name, num_files=1, file_type='mindrecord'):
        """
        Save the dynamic data processed by the dataset pipeline in common dataset format.
        Supported dataset formats: 'mindrecord' only

        Implicit type casting exists when saving data as 'mindrecord'. The table below shows how to do type casting.

        .. list-table:: Implicit Type Casting when Saving as 'mindrecord'
           :widths: 25 25 50
           :header-rows: 1

           * - Type in 'dataset'
             - Type in 'mindrecord'
             - Details
           * - bool
             - None
             - Not supported
           * - int8
             - int32
             -
           * - uint8
             - bytes(1D uint8)
             - Drop dimension
           * - int16
             - int32
             -
           * - uint16
             - int32
             -
           * - int32
             - int32
             -
           * - uint32
             - int64
             -
           * - int64
             - int64
             -
           * - uint64
             - None
             - Not supported
           * - float16
             - float32
             -
           * - float32
             - float32
             -
           * - float64
             - float64
             -
           * - string
             - string
             - Multi-dimensional string not supported

        Note:
            1. To save the samples in order, set dataset's shuffle to False and num_files to 1.
            2. Before calling the function, do not use batch operator, repeat operator or data augmentation operators
               with random attribute in map operator.
            3. Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) nor
               multi-dimensional DE_STRING.

        Args:
            file_name (str): Path to dataset file.
            num_files (int, optional): Number of dataset files (default=1).
            file_type (str, optional): Dataset format (default='mindrecord').

        """

        if num_files == 1:
            file_names = [file_name]
        else:
            suffix = len(str(num_files - 1))
            file_names = ["{}{}".format(file_name, str(x).rjust(suffix, '0'))
                          for x in range(num_files)]

        return SaveOp(self).save(file_names, file_type)

    @check_iterator
    def create_tuple_iterator(self, columns=None, num_epochs=-1, output_numpy=False):
        """
        Create an iterator over the dataset. The data retrieved will be a list of ndarrays of data.

        To specify which columns to list and the order needed, use columns_list. If columns_list
        is not provided, the order of the columns will not be changed.

        Args:
            columns (list[str], optional): List of columns to be used to specify the order of columns
                (default=None, means all columns).
            num_epochs (int, optional): Maximum number of epochs that iterator can be iterated.
                (default=-1, iterator can be iterated infinite number of epochs)
            output_numpy (bool, optional): Whether or not to output NumPy datatype.
                If output_numpy=False, iterator will output MSTensor (default=False).

        Returns:
            Iterator, list of ndarrays.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object
            >>>
            >>> # Create an iterator
            >>> # The columns in the data obtained by the iterator will not be changed.
            >>> iterator = data.create_tuple_iterator()
            >>> for item in iterator:
            >>>     # convert the returned tuple to a list and print
            >>>     print(list(item))
        """
        if output_numpy is None:
            output_numpy = False

        if self._noop_mode():
            return DummyIterator(self, 'tuple')
        return TupleIterator(self, columns, num_epochs, output_numpy)

    @check_iterator
    def create_dict_iterator(self, num_epochs=-1, output_numpy=False):
        """
        Create an iterator over the dataset. The data retrieved will be a dictionary.

        The order of the columns in the dictionary may not be the same as the original order.

        Args:
            num_epochs (int, optional): Maximum number of epochs that iterator can be iterated
                (default=-1, iterator can be iterated infinite number of epochs).
            output_numpy (bool, optional): Whether or not to output NumPy datatype,
                if output_numpy=False, iterator will output MSTensor (default=False).

        Returns:
            Iterator, dictionary of column name-ndarray pair.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> # data is an instance of Dataset object
            >>>
            >>> # create an iterator
            >>> # The columns in the data obtained by the iterator might be changed.
            >>> iterator = data.create_dict_iterator()
            >>> for item in iterator:
            >>>     # print the data in column1
            >>>     print(item["column1"])
        """
        if output_numpy is None:
            output_numpy = False

        if self._noop_mode():
            return DummyIterator(self, 'dict')
        return DictIterator(self, num_epochs, output_numpy)

    def __iter__(self):
        """Create an iterator over the dataset."""
        return self.create_tuple_iterator(num_epochs=1)

    @property
    def input_indexs(self):
        return self._input_indexs

    @input_indexs.setter
    def input_indexs(self, value):
        self._input_indexs = value

    def _get_pipeline_info(self):
        """
        Get pipeline information.
        """
        device_iter = TupleIterator(self)
        self._output_shapes = device_iter.get_output_shapes()
        self._output_types = device_iter.get_output_types()
        self._batch_size = device_iter.get_batch_size()
        self._num_classes = device_iter.num_classes()
        self._repeat_count = device_iter.get_repeat_count()
        device_iter.stop()

    def get_col_names(self):
        """
        Get names of the columns in the dataset
        """
        return Iterator(self).get_col_names()

    def output_shapes(self):
        """
        Get the shapes of output data.

        Return:
            List, list of shapes of each column.
        """
        if self._output_shapes is None:
            self._get_pipeline_info()
        return self._output_shapes

    def output_types(self):
        """
        Get the types of output data.

        Return:
            List of data types.
        """
        if self._output_types is None:
            self._get_pipeline_info()
        return self._output_types

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            if self.children:
                self.dataset_size = self.children[0].get_dataset_size()
        return self.dataset_size

    def num_classes(self):
        """
        Get the number of classes in a dataset.

        Return:
            Number, number of classes.
        """
        if self.children:
            return self.children[0].num_classes()
        return None

    def get_sync_notifiers(self):
        if self.children:
            return self.children[0].get_sync_notifiers()
        return {}

    def disable_sync(self):
        if self.children:
            return self.children[0].disable_sync()
        return {}

    def is_sync(self):
        if self.children:
            return self.children[0].is_sync()
        return False

    def sync_update(self, condition_name, num_batch=None, data=None):
        """
        Release a blocking condition and trigger callback with given data.

        Args:
            condition_name (str): The condition name that is used to toggle sending next row.
            num_batch (Union[int, None]): The number of batches (rows) that are released.
                When num_batch is None, it will default to the number specified by the
                sync_wait operator (default=None).
            data (Union[dict, None]): The data passed to the callback (default=None).
        """
        if isinstance(num_batch, int) and num_batch <= 0:
            # throwing exception, disable all sync_wait in pipeline
            self.disable_sync()
            raise RuntimeError("Sync_update batch size can only be positive, got : {}".format(num_batch))
        notifiers_dict = self.get_sync_notifiers()
        if condition_name not in notifiers_dict:
            # throwing exception, disable all sync_wait in pipeline
            self.disable_sync()
            raise RuntimeError("Condition name not found")
        if num_batch is not None:
            num_batch *= self.get_batch_size()
        notifiers_dict[condition_name](num_batch, data)

    def get_batch_size(self):
        """
        Get the size of a batch.

        Return:
            Number, the number of data in a batch.
        """
        if self.children:
            return self.children[0].get_batch_size()
        return 1

    def get_repeat_count(self):
        """
        Get the replication times in RepeatDataset else 1.

        Return:
            Number, the count of repeat.
        """
        if self.children:
            return self.children[0].get_repeat_count()
        return 1

    def reset(self):
        """Reset the dataset for next epoch."""

    def is_shuffled(self):
        for input_dataset in self.children:
            if input_dataset.is_shuffled():
                return True

        return False

    def is_sharded(self):
        for input_dataset in self.children:
            if input_dataset.is_sharded():
                return True

        return False


class SourceDataset(Dataset):
    """
    Abstract class to represent a source dataset which produces content to the data pipeline.
    """

    # No need for __init__ since it is the same as the super's init

    @staticmethod
    def _find_files(patterns):
        """
        Utility function to search for files with the given glob patterns.

        Args:
            patterns (Union[str, list[str]]): String or list of patterns to be searched.

        Returns:
            List, files.
        """

        if not isinstance(patterns, list):
            patterns = [patterns]

        file_list = []
        unmatched_patterns = []
        for pattern in patterns:
            matches = [match for match in glob.glob(pattern, recursive=True) if os.path.isfile(match)]

            if matches:
                file_list.extend(matches)
            else:
                unmatched_patterns.append(pattern)

        if unmatched_patterns:
            raise ValueError("The following patterns did not match any files: ", unmatched_patterns)

        if file_list:  # not empty
            return file_list
        raise ValueError("The list of path names matching the patterns is empty.")

    def is_shuffled(self):
        raise NotImplementedError("SourceDataset must implement is_shuffled.")

    def is_sharded(self):
        raise NotImplementedError("SourceDataset must implement is_sharded.")


class MappableDataset(SourceDataset):
    """
    Abstract class to represent a source dataset which supports use of samplers.
    """

    def __init__(self, num_parallel_workers=None):
        # check if all subclasses use this name
        super().__init__(num_parallel_workers)
        self.sampler = None

    def add_sampler(self, new_sampler):
        # note: By adding a sampler, the sampled IDs will flow to new_sampler
        # after first passing through the current samplers attached to this dataset.
        if self.dataset_size is not None:
            self.dataset_size = None
        new_sampler.add_child(self.sampler)
        self.sampler = new_sampler

    def use_sampler(self, new_sampler):
        """
        Will make the current dataset use the new_sampler provided.

        Args:
            new_sampler (Sampler): The sampler to use for the current dataset.

        Returns:
            Dataset, that uses new_sampler.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> dataset_dir = "/path/to/imagefolder_directory"
            >>> # Note: A SequentialSampler is created by default
            >>> data = ds.ImageFolderDataset(dataset_dir)
            >>>
            >>> # Use a DistributedSampler instead of the SequentialSampler
            >>> new_sampler = ds.DistributedSampler(10, 2)
            >>> data.use_sampler(new_sampler)
        """
        if new_sampler is None:
            raise TypeError("Input sampler can not be None.")
        if not isinstance(new_sampler, (samplers.BuiltinSampler, samplers.Sampler)):
            raise TypeError("Input sampler is not an instance of a sampler.")
        if self.dataset_size is not None:
            self.dataset_size = None

        self.sampler = self.sampler.child_sampler
        self.add_sampler(new_sampler)

    def is_shuffled(self):
        raise NotImplementedError("MappableDataset must implement is_shuffled.")

    def is_sharded(self):
        raise NotImplementedError("MappableDataset must implement is_sharded.")

    def _get_sampler_dataset_size(self):
        if self.sampler is not None:
            if hasattr(self.sampler, 'get_num_samples'):
                return self.sampler.get_num_samples()
            if hasattr(self.sampler, '__len__'):
                return len(self.sampler)

        return None

    @check_split
    def split(self, sizes, randomize=True):
        """
        Split the dataset into smaller, non-overlapping datasets.

        Args:
            sizes (Union[list[int], list[float]]): If a list of integers [s1, s2, …, sn] is
                provided, the dataset will be split into n datasets of size s1, size s2, …, size sn
                respectively. If the sum of all sizes does not equal the original dataset size, an
                error will occur.
                If a list of floats [f1, f2, …, fn] is provided, all floats must be between 0 and 1
                and must sum to 1, otherwise an error will occur. The dataset will be split into n
                Datasets of size round(f1*K), round(f2*K), …, round(fn*K) where K is the size of the
                original dataset.
                If after rounding:

                    - Any size equals 0, an error will occur.

                    - The sum of split sizes < K, the difference will be added to the first split.

                    - The sum of split sizes > K, the difference will be removed from the first large
                      enough split such that it will have atleast 1 row after removing the difference.

            randomize (bool, optional): Determines whether or not to split the data randomly (default=True).
                If True, the data will be randomly split. Otherwise, each split will be created with
                consecutive rows from the dataset.

        Note:
            1. There is an optimized split function, which will be called automatically when the dataset
               that calls this function is a MappableDataset.
            2. Dataset should not be sharded if split is going to be called. Instead, create a
               DistributedSampler and specify a split to shard after splitting. If dataset is
               sharded after a split, it is strongly recommended to set the same seed in each instance
               of execution, otherwise each shard may not be part of the same split (see Examples).
            3. It is strongly recommended to not shuffle the dataset, but use randomize=True instead.
               Shuffling the dataset may not be deterministic, which means the data in each split
               will be different in each epoch. Furthermore, if sharding occurs after split, each
               shard may not be part of the same split.

        Raises:
            RuntimeError: If get_dataset_size returns None or is not supported for this dataset.
            RuntimeError: If sizes is list of integers and sum of all elements in sizes does not
                equal the dataset size.
            RuntimeError: If sizes is list of float and there is a split with size 0 after calculations.
            RuntimeError: If the dataset is sharded prior to calling split.
            ValueError: If sizes is list of float and not all floats are between 0 and 1, or if the
                floats don’t sum to 1.

        Returns
            tuple(Dataset), a tuple of datasets that have been split.

        Examples:
            >>> import mindspore.dataset as ds
            >>>
            >>> dataset_dir = "/path/to/imagefolder_directory"
            >>>
            >>> # Since many datasets have shuffle on by default, set shuffle to False if split will be called!
            >>> data = ds.ImageFolderDataset(dataset_dir, shuffle=False)
            >>>
            >>> # Set the seed, and tell split to use this seed when randomizing.
            >>> # This is needed because sharding will be done later
            >>> ds.config.set_seed(58)
            >>> train, test = data.split([0.9, 0.1])
            >>>
            >>> # To shard the train dataset, use a DistributedSampler
            >>> train_sampler = ds.DistributedSampler(10, 2)
            >>> train.use_sampler(train_sampler)
        """
        if self.is_shuffled():
            logger.warning("Dataset is shuffled before split.")

        if self.is_sharded():
            raise RuntimeError("Dataset should not be sharded before split.")

        absolute_sizes = self._get_absolute_split_sizes(sizes)
        splits = []
        current_split_start_index = 0
        for size in absolute_sizes:
            ds = copy.deepcopy(self)
            ds.dataset_size = None
            if randomize:
                # want to shuffle the same way every epoch before split, we are assuming
                # that the user will call set_seed
                random_sampler = samplers.RandomSampler()
                random_sampler.reshuffle_each_epoch = False
                ds.add_sampler(random_sampler)

            subset_sampler = samplers.SequentialSampler(current_split_start_index, size)
            ds.add_sampler(subset_sampler)

            # add sequential sampler, so that if user calls use_sampler, we will
            # get rid of the sequential sampler instead of something we need
            ds.add_sampler(samplers.SequentialSampler())

            splits.append(ds)

            current_split_start_index += size

        return tuple(splits)


class DatasetOp(Dataset):
    """
    Abstract class to represent an operation on a dataset.
    """

    # No need for __init__ since it is the same as the super's init
    def get_class_indexing(self):
        """
        Get the class index.

        Return:
            Dict, A str-to-int mapping from label name to index.
        """
        if self.children:
            return self.children[0].get_class_indexing()
        raise NotImplementedError("Dataset {} has not supported api get_class_indexing yet.".format(type(self)))


class BucketBatchByLengthDataset(DatasetOp):
    """
    The result of applying BucketBatchByLength operator to the input dataset.
    """

    def __init__(self, input_dataset, column_names, bucket_boundaries, bucket_batch_sizes,
                 element_length_function, pad_info, pad_to_bucket_boundary, drop_remainder):
        super().__init__()

        self.column_names = column_names
        self.bucket_boundaries = bucket_boundaries
        self.bucket_batch_sizes = bucket_batch_sizes
        self.element_length_function = element_length_function
        self.pad_info = pad_info
        self.pad_to_bucket_boundary = pad_to_bucket_boundary
        self.drop_remainder = drop_remainder

        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs

    def get_args(self):
        args = super().get_args()
        args["length_dependent_columns"] = self.column_names
        args["bucket_boundaries"] = self.bucket_boundaries
        args["bucket_batch_sizes"] = self.bucket_batch_sizes
        args["element_length_function"] = self.element_length_function
        args["pad_info"] = self.pad_info
        args["pad_to_bucket_boundary"] = self.pad_to_bucket_boundary
        args["drop_remainder"] = self.drop_remainder
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = 0
            for _ in self.create_dict_iterator(num_epochs=1, output_numpy=True):
                num_rows += 1
            self.dataset_size = num_rows
        return self.dataset_size


class BatchDataset(DatasetOp):
    """
    The result of applying Batch operator to the input dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be batched.
        batch_size (Union[int, function]): The number of rows each batch is created with. An
            int or callable which takes exactly 1 parameter, BatchInfo.
        drop_remainder (bool, optional): Determines whether or not to drop the last
            possibly incomplete batch (default=False). If True, and if there are less
            than batch_size rows available to make the last batch, then those rows will
            be dropped and not propagated to the child node.
        num_parallel_workers (int, optional): Number of workers to process the dataset in parallel (default=None).
        per_batch_map (callable, optional): Per batch map callable. A callable which takes
            (list[Tensor], list[Tensor], ..., BatchInfo) as input parameters. Each list[Tensor] represents a batch of
            Tensors on a given column. The number of lists should match with number of entries in input_columns. The
            last parameter of the callable must always be a BatchInfo object.
        input_columns (list[str], optional): List of names of the input columns. The size of the list must
            match with signature of per_batch_map callable.
        output_columns (list[str], optional): List of names assigned to the columns outputted by
            the last operation. This parameter is mandatory if len(input_columns) !=
            len(output_columns). The size of this list must match the number of output
            columns of the last operation. (default=None, output columns will have the same
            name as the input columns, i.e., the columns will be replaced).
        column_order (list[str], optional): List of all the desired columns to propagate to the
            child node. This list must be a subset of all the columns in the dataset after
            all operations are applied. The order of the columns in each row propagated to the
            child node follow the order they appear in this list. The parameter is mandatory
            if the len(input_columns) != len(output_columns). (default=None, all columns
            will be propagated to the child node, the order of the columns will remain the
            same).
        pad_info (dict, optional): Whether to perform padding on selected columns. pad_info={"col1":([224,224],0)}
            will pad column with name "col1" to a tensor of size [224,224] and fill the missing with 0.

    """

    def __init__(self, input_dataset, batch_size, drop_remainder=False, num_parallel_workers=None,
                 per_batch_map=None, input_columns=None, output_columns=None, column_order=None, pad_info=None):
        super().__init__(num_parallel_workers)

        if BatchDataset._is_ancestor_of_repeat(input_dataset):
            logger.warning("Repeat is located before batch, data from two epochs can be batched together.")

        BatchDataset._update_batch_size_for_syncwait(input_dataset, batch_size)

        self.batch_size = batch_size
        self.drop_remainder = drop_remainder
        self.per_batch_map = per_batch_map
        self.input_columns = input_columns if not isinstance(input_columns, str) else [input_columns]
        self.output_columns = output_columns if not isinstance(output_columns, str) else [output_columns]
        self.column_order = column_order if not isinstance(column_order, str) else [column_order]
        self.pad_info = pad_info
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs

    def get_args(self):
        args = super().get_args()
        args["batch_size"] = self.batch_size
        args["drop_remainder"] = self.drop_remainder
        args["per_batch_map"] = self.per_batch_map
        args["input_columns"] = self.input_columns
        args["output_columns"] = self.output_columns
        args["column_order"] = self.column_order
        args["pad_info"] = self.pad_info
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            child_size = self.children[0].get_dataset_size()
            if child_size is not None and isinstance(self.batch_size, int):
                if self.drop_remainder:
                    self.dataset_size = math.floor(child_size / self.batch_size)
                else:
                    self.dataset_size = math.ceil(child_size / self.batch_size)
        return self.dataset_size

    def get_batch_size(self):
        """
        Get the size of a batch.

        Return:
            Number, the number of data in a batch.
        """
        return self.batch_size

    @staticmethod
    def _is_ancestor_of_repeat(dataset):
        """
        Utility function to find the case where repeat is used before batch.

        Args:
             dataset (Dataset): Dataset to be checked.
        Return:
            True or False.
        """
        if isinstance(dataset, RepeatDataset):
            return True
        flag = False
        for input_dataset in dataset.children:
            flag = flag | BatchDataset._is_ancestor_of_repeat(input_dataset)
        return flag

    @staticmethod
    def _update_batch_size_for_syncwait(dataset, batch_size):
        """
        Utility function to notify batch size to sync_wait.

        Args:
             dataset (Dataset): Dataset to be checked.
             batch_size (int): batch size to notify.
        """
        if isinstance(dataset, SyncWaitDataset):
            dataset.update_sync_batch_size(batch_size)
        for input_dataset in dataset.children:
            BatchDataset._update_batch_size_for_syncwait(input_dataset, batch_size)


class BatchInfo(CBatchInfo):
    """
    The information object associates with the current batch of tensors.
    """

    def get_batch_num(self):
        """
        Return the batch number of the current batch.

        Return:
            Number, number of the current batch.
        """
        return

    def get_epoch_num(self):
        """
        Return the epoch number of the current batch.

        Return:
            Number, number of the current epoch.
        """
        return


class BlockReleasePair:
    """
    The blocking condition class used by SyncWaitDataset.

    Args:
        init_release_rows (int): Number of lines to allow through the pipeline.
        callback (function): The callback function that will be called when release is called.
    """

    def __init__(self, init_release_rows, callback=None):
        if isinstance(init_release_rows, int) and init_release_rows <= 0:
            raise ValueError("release_rows need to be greater than 0.")
        self.row_count = -init_release_rows
        self.cv = threading.Condition()
        self.callback = callback
        self.default_rows = init_release_rows
        self.disable = False

    def __deepcopy__(self, memodict):
        if id(self) in memodict:
            return memodict[id(self)]
        memodict[id(self)] = self
        # condition variable and callback are the same, but reset the counter
        self.reset()
        return self

    def reset(self):
        with self.cv:
            self.row_count = -self.default_rows
            self.cv.notify_all()

    def update_batched_size(self, batch_size):
        # sanity check
        if isinstance(batch_size, int) and batch_size <= 0:
            raise ValueError("batch_size need to be greater than 0.")

        # should only use before the pipeline creates
        self.row_count *= batch_size
        self.default_rows *= batch_size

    def block_func(self):
        """
        Function for handing blocking condition.

        Return:
            True
        """
        with self.cv:
            # if disable is true, the always evaluate to true
            not_time_out = self.cv.wait_for(lambda: (self.row_count < 0 or self.disable),
                                            timeout=get_callback_timeout())
            # time_out will be False if time out occurs
            if not not_time_out:
                logger.warning("Timeout happened in sync_wait, disabling lock")
                self.disable = True
            self.row_count += 1
        return True

    def release_func(self, pass_rows=None, data=None):
        with self.cv:
            if pass_rows is None:
                pass_rows = self.default_rows
            self.row_count -= pass_rows
            if self.callback is not None:
                self.callback(data)
            self.cv.notify_all()

    def disable_lock(self):
        with self.cv:
            self.disable = True
            self.cv.notify_all()


class SyncWaitDataset(DatasetOp):
    """
    The result of adding a blocking condition to the input Dataset.

    Args:
        input_dataset (Dataset): Input dataset to apply flow control.
        num_batch (int): Number of batches without blocking at the start of each epoch.
        condition_name (str): Condition name that is used to toggle sending next row.
        callback (function): Callback function that will be invoked when sync_update is called.

    Raises:
        RuntimeError: If condition name already exists.
    """

    def __init__(self, input_dataset, condition_name, num_batch, callback=None):
        super().__init__()
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        # set to the default value, waiting for the batch to update it
        self._condition_name = condition_name
        if isinstance(num_batch, int) and num_batch <= 0:
            raise ValueError("num_batch need to be greater than 0.")

        self._pair = BlockReleasePair(num_batch, callback)
        if self._condition_name in self.children[0].get_sync_notifiers():
            raise RuntimeError("Condition name is already in use")
        logger.warning("Please remember to add dataset.sync_update(condition=%s), otherwise will result in hanging",
                       condition_name)

    def get_sync_notifiers(self):
        return {**self.children[0].get_sync_notifiers(), **{self._condition_name: self._pair.release_func}}

    def is_sync(self):
        return True

    def get_args(self):
        args = super().get_args()
        args["condition_name"] = self._condition_name
        args["condition_func"] = self._pair.block_func
        return args

    def update_sync_batch_size(self, batch_size):
        if isinstance(batch_size, int) and batch_size <= 0:
            raise ValueError("num_batch need to be greater than 0.")
        self._pair.update_batched_size(batch_size)

    def disable_sync(self):
        logger.info("Disabling Sync")
        self._pair.disable_lock()

    @staticmethod
    def _is_ancestor_of_batch(dataset):
        """
        Utility function to find the case where sync_wait is used before batch.

        Args:
             dataset (Dataset): Dataset to be checked.
        Return:
            True or False.
        """
        if isinstance(dataset, BatchDataset):
            return True
        flag = False
        for input_dataset in dataset.children:
            flag = flag | SyncWaitDataset._is_ancestor_of_batch(input_dataset)
        return flag


class ShuffleDataset(DatasetOp):
    """
    The result of applying Shuffle operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be shuffled.
        buffer_size (int): Size of the buffer.

    Raises:
        RuntimeError: If exist sync operators before shuffle.
    """

    def __init__(self, input_dataset, buffer_size):
        super().__init__()
        self.buffer_size = buffer_size
        self.children.append(input_dataset)
        self.reshuffle_each_epoch = None
        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs
        if self.is_sync():
            raise RuntimeError("No shuffle after sync operators")

    def get_args(self):
        args = super().get_args()
        args["buffer_size"] = self.buffer_size
        if self.reshuffle_each_epoch is not None:
            args["reshuffle_each_epoch"] = self.reshuffle_each_epoch

        return args

    def is_shuffled(self):
        return True


# Pyfunc collection for multiprocess pyfunc
# This global variable will only be used within subprocesses
_GLOBAL_PYFUNC_LIST = []


# Pyfunc worker init function
# Python multiprocessing library forbid sending lambda function through pipe.
# This init function allow us to add all Python function to a global collection and then fork afterwards.
def _pyfunc_worker_init(pyfunc_list):
    global _GLOBAL_PYFUNC_LIST
    _GLOBAL_PYFUNC_LIST = pyfunc_list


# Pyfunc worker execution function
# All exceptions will be raised to main processes
def _pyfunc_worker_exec(index, *args):
    try:
        return _GLOBAL_PYFUNC_LIST[index](*args)
    except KeyboardInterrupt:
        raise Exception("Multiprocess MapOp worker receives KeyboardInterrupt")


# PythonCallable wrapper for multiprocess pyfunc
class _PythonCallable:
    """
    Internal Python function wrapper for multiprocessing pyfunc.
    """

    def __init__(self, py_callable, idx, pool=None):
        # Original Python callable from user.
        self.py_callable = py_callable
        # Process pool created for current iterator.
        self.pool = pool
        # Python callable index for subprocess _GLOBAL_PYFUNC_LIST
        self.idx = idx

    def __call__(self, *args):
        if self.pool is not None:
            # This call will send the tensors along with Python callable index to the process pool.
            # Block, yield GIL. Current thread will reacquire GIL once result is returned.
            result = self.pool.apply_async(_pyfunc_worker_exec, [self.idx, *args])
            while check_iterator_cleanup() is False:
                try:
                    return result.get(30)
                except multiprocessing.TimeoutError:
                    continue
                except KeyboardInterrupt:
                    self.pool.terminate()
                    self.pool.join()
                    raise Exception("Multiprocess MapOp worker receives KeyboardInterrupt")
            return (None,)
        # Invoke original Python callable in master process in case the pool is gone.
        return self.py_callable(*args)


class MapDataset(DatasetOp):
    """
    The result of applying the Map operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be mapped.
        operations (TensorOp): A function mapping a nested structure of tensors
            to another nested structure of tensor (default=None).
        input_columns (list[str]): List of names of the input columns
            (default=None, the operations will be applied on the first columns in the dataset).
            The size of the list should match the number of inputs of the first operator.
        output_columns (list[str], optional): List of names of the output columns.
            The size of the list should match the number of outputs of the last operator
            (default=None, output columns will be the input columns, i.e., the columns will
            be replaced).
        column_order (list[str], optional): List of all the desired columns of the dataset (default=None).
            The argument is mandatory if len(input_columns) != len(output_columns).
        num_parallel_workers (int, optional): Number of workers to process the dataset
            in parallel (default=None).
        python_multiprocessing (bool, optional): Parallelize Python operations with multiple worker process. This
            option could be beneficial if the Python operation is computational heavy (default=False).
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.
        callbacks: (DSCallback, list[DSCallback], optional): List of Dataset callbacks to be called (Default=None)

        Raises:
            ValueError: If len(input_columns) != len(output_columns) and column_order is not specified.
    """

    def __init__(self, input_dataset, operations=None, input_columns=None, output_columns=None, column_order=None,
                 num_parallel_workers=None, python_multiprocessing=False, cache=None, callbacks=None):
        super().__init__(num_parallel_workers)
        self.children.append(input_dataset)
        if operations is not None:
            if not isinstance(operations, list):
                operations = [operations]
            elif isinstance(operations, list) and len(operations) > 1:
                # wraps adjacent Python operations in a Compose to allow mixing of Python and C++ operations
                new_ops, start_ind, end_ind = [], 0, 0
                for i, op in enumerate(operations):
                    if not callable(op):
                        # reset counts
                        if start_ind != end_ind:
                            new_ops.append(py_transforms.Compose(operations[start_ind:end_ind]))
                        new_ops.append(op)
                        start_ind, end_ind = i + 1, i + 1
                    else:
                        end_ind += 1
                # do additional check in case the last operation is a Python operation
                if start_ind != end_ind:
                    new_ops.append(py_transforms.Compose(operations[start_ind:end_ind]))
                operations = new_ops
        self.operations = operations
        if input_columns is not None and not isinstance(input_columns, list):
            input_columns = [input_columns]
        self.input_columns = input_columns
        if output_columns is not None and not isinstance(output_columns, list):
            output_columns = [output_columns]
        self.output_columns = output_columns
        self.cache = cache
        self.column_order = column_order

        if self.input_columns and self.output_columns \
                and len(self.input_columns) != len(self.output_columns) \
                and self.column_order is None:
            raise ValueError("When (len(input_columns) != len(output_columns)), column_order must be specified.")

        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs
        self.python_multiprocessing = python_multiprocessing
        self.process_pool = None

        if callbacks is not None and not isinstance(callbacks, list):
            callbacks = [callbacks]

        self.callbacks = callbacks

    def get_args(self):
        args = super().get_args()
        args["input_columns"] = self.input_columns
        args["operations"] = self.operations
        args["output_columns"] = self.output_columns
        args["column_order"] = self.column_order
        args["cache"] = self.cache.cache_client if self.cache is not None else None

        if self.callbacks is not None:
            args["callbacks"] = [cb.create_runtime_obj() for cb in self.callbacks]
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            self.dataset_size = self.children[0].get_dataset_size()
        return self.dataset_size

    def __deepcopy__(self, memodict):
        if id(self) in memodict:
            return memodict[id(self)]
        cls = self.__class__
        new_op = cls.__new__(cls)
        memodict[id(self)] = new_op
        new_op.children = copy.deepcopy(self.children, memodict)
        new_op.input_columns = copy.deepcopy(self.input_columns, memodict)
        new_op.output_columns = copy.deepcopy(self.output_columns, memodict)
        new_op.column_order = copy.deepcopy(self.column_order, memodict)
        new_op.num_parallel_workers = copy.deepcopy(self.num_parallel_workers, memodict)
        new_op.parent = copy.deepcopy(self.parent, memodict)
        new_op.input_indexs = copy.deepcopy(self._input_indexs, memodict)
        new_op.python_multiprocessing = copy.deepcopy(self.python_multiprocessing, memodict)
        new_op.cache = copy.deepcopy(self.cache, memodict)
        new_op.operations = self.operations
        new_op.dataset_size = self.dataset_size
        new_op.callbacks = self.callbacks
        return new_op

    # Iterator bootstrap will be called on iterator construction.
    # A deep copy of Dataset object is created prior of iterator_bootstrap.
    # This method will create per iterator process pool and bind pyfunc execution to the pool.
    def iterator_bootstrap(self):
        """
        Per iterator bootstrap callback.
        """
        if self.python_multiprocessing:
            iter_specific_operations = []
            callable_list = []

            # Pass #1, look for Python callables and build list
            for op in self.operations:
                if callable(op):
                    callable_list.append(op)

            if callable_list:
                # Construct pool with the callable list
                # The callable list and _pyfunc_worker_init are used to pass lambda function in to subprocesses
                self.process_pool = multiprocessing.Pool(processes=self.num_parallel_workers,
                                                         initializer=_pyfunc_worker_init,
                                                         initargs=(callable_list,))
                # Pass #2
                idx = 0
                for op in self.operations:
                    if callable(op):
                        # Wrap Python callable into _PythonCallable
                        iter_specific_operations.append(_PythonCallable(op, idx, self.process_pool))
                        idx += 1
                    else:
                        # CPP ops remain the same
                        iter_specific_operations.append(op)
                self.operations = iter_specific_operations

    def __del__(self):
        if hasattr(self, 'process_pool') and self.process_pool is not None:
            self.process_pool.terminate()


class FilterDataset(DatasetOp):
    """
    The result of applying filter predicate to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be mapped.
        predicate (callable): Python callable which returns a boolean value. If False then filter the element.
        input_columns (list[str], optional): List of names of the input columns
        (default=None, the predicate will be applied to all columns in the dataset).
        num_parallel_workers (int, optional): Number of workers to process the dataset
            in parallel (default=None).
    """

    def __init__(self, input_dataset, predicate, input_columns=None, num_parallel_workers=None):
        super().__init__(num_parallel_workers)
        self.predicate = lambda *args: bool(predicate(*args))
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        if input_columns is not None and not isinstance(input_columns, list):
            input_columns = [input_columns]
        self.input_columns = input_columns

    def get_args(self):
        args = super().get_args()
        args["predicate"] = self.predicate
        args["input_columns"] = self.input_columns
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, num of batches.
        """
        if self.dataset_size is None:
            num_rows = 0
            for _ in self.create_dict_iterator(num_epochs=1, output_numpy=True):
                num_rows += 1
            self.dataset_size = num_rows
        return self.dataset_size


class RepeatDataset(DatasetOp):
    """
    The result of applying Repeat operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be repeated.
        count (int): Number of times the dataset will be repeated (default=-1, repeat indefinitely).
    """

    def __init__(self, input_dataset, count):
        super().__init__()
        if count is None:
            self.count = -1
        else:
            self.count = count
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs

    def get_args(self):
        args = super().get_args()
        args["count"] = self.count
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            child_size = self.children[0].get_dataset_size()
            if child_size is not None:
                self.dataset_size = child_size * self.count
        return self.dataset_size

    def get_repeat_count(self):
        """
        Get the replication times in RepeatDataset.

        Return:
            Number, the count of repeat.
        """
        return self.count


class SkipDataset(DatasetOp):
    """
    The result of applying Skip operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input dataset to have elements skipped.
        count (int): Number of elements to be skipped in the dataset.
    """

    def __init__(self, input_dataset, count):
        super().__init__()
        self.count = count
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs

    def get_args(self):
        args = super().get_args()
        args["count"] = self.count
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            child_size = self.children[0].get_dataset_size()
            self.dataset_size = 0
            if self.count >= 0 and self.count < child_size:
                self.dataset_size = child_size - self.count
        return self.dataset_size


class TakeDataset(DatasetOp):
    """
    The result of applying Take operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to have elements taken from.
        count (int): Number of elements to be taken from the dataset.
    """

    def __init__(self, input_dataset, count):
        super().__init__()
        self.count = count
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs

    def get_args(self):
        args = super().get_args()
        args["count"] = self.count
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            child_size = self.children[0].get_dataset_size()
            if child_size < self.count:
                self.dataset_size = child_size
            else:
                self.dataset_size = self.count
        return self.dataset_size


class ZipDataset(DatasetOp):
    """
    The result of applying Zip operator to the input Dataset.

    Args:
        datasets (tuple): A tuple of datasets to be zipped together.

    Raises:
        TypeError: If dataset is not an instance of Dataset.
    """

    def __init__(self, datasets):
        super().__init__()
        for dataset in datasets:
            if not isinstance(dataset, Dataset):
                raise TypeError("The parameter %s of zip has type error!" % (dataset))
        self.datasets = datasets
        for data in datasets:
            self.children.append(data)
            data.parent.append(self)

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            children_sizes = [c.get_dataset_size() for c in self.children]
            if all(c is not None for c in children_sizes):
                self.dataset_size = min(children_sizes)
        return self.dataset_size

    def num_classes(self):
        """
        Get the number of classes in a dataset.

        Return:
            Number, number of classes.
        """
        return None

    def is_sync(self):
        return any([c.is_sync() for c in self.children])

    def get_args(self):
        args = super().get_args()
        return args


class ConcatDataset(DatasetOp):
    """
    The result of applying concat dataset operator to the input Dataset.

    Args:
        datasets (list): A list of datasets to be concatenated together.

    Raises:
        TypeError: If dataset is not an instance of Dataset.
        ValueError: If there is no samples in the one of the datasets.
    """

    def __init__(self, datasets):
        super().__init__()
        for dataset in datasets:
            if not isinstance(dataset, Dataset):
                raise TypeError("The parameter %s of concat has type error!" % (dataset))
        self.datasets = datasets
        self._sampler = None
        for data in datasets:
            self.children.append(data)
            data.parent.append(self)

        self.children_sizes_ = [c.get_dataset_size() for c in self.children]
        child_index = 0
        for item in self.children_sizes_:
            if item == 0:
                raise ValueError("There is no samples in the %dth dataset. Please make sure there are "
                                 "valid samples in the dataset" % child_index)
            child_index += 1

        # _children_flag_and_nums: A list of pair<int ,int>.The first element of pair is flag that characterizes
        # whether the data set is mappable. The second element of pair is length of the dataset
        self._children_flag_and_nums = []

        # _children_start_end_index_: A list of pair<int ,int>.The elements of pair are used to characterize
        # the valid position of the dataset corresponding to the subscript when sampling
        self._children_start_end_index_ = []
        for index, child in enumerate(self.children):
            tem_list = [-1, -1]
            self._children_start_end_index_.append(tem_list)
            dataset_len = self.children_sizes_[index]
            if isinstance(child, GeneratorDataset) and not hasattr(child.source, "__getitem__"):
                dataset_len = 0
                self.children_sizes_[index] = 0

            if isinstance(child, MappableDataset):
                self._children_flag_and_nums.append((0, dataset_len))
            else:
                self._children_flag_and_nums.append((1, dataset_len))

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = 0
            for _ in self.create_dict_iterator(num_epochs=1, output_numpy=True):
                num_rows += 1
            self.dataset_size = num_rows
        return self.dataset_size

    def use_sampler(self, sampler):
        """
        Set the distributedSampler to concat dataset

        Args:
            sampler (Sampler): The sampler to use for the current dataset.
                Currently supported: DistributedSampler.

        Raises:
            TypeError: If the sampler is not an instance of DistributedSampler
            ValueError: If the parameter shuffle of sampler is True
            ValueError: If the parameter NumSamples of sampler is not None.
            ValueError: If num_shards <=0.
        """
        if not isinstance(sampler, samplers.DistributedSampler):
            raise TypeError("The parameter %s of concat must be DistributedSampler!" % (sampler))

        if sampler.is_shuffled():
            raise ValueError("The parameter shuffle of DistributedSampler must to be False!")

        if sampler.num_shards <= 0:
            raise ValueError("The parameter num_shards of DistributedSampler must be positive int!")

        if sampler.get_num_samples() is not None:
            raise ValueError("The parameter num_samples of DistributedSampler is not support to be set!")

        self._sampler = _select_sampler(None, sampler, None, None, None)
        cumulative_samples_nums = 0
        for index, child in enumerate(self.children):
            if hasattr(child, 'sampler') and child.sampler.get_num_samples() is not None:
                raise ValueError("The parameter NumSamples of %s is not support to be set!" % (child))

            if isinstance(child, BatchDataset):
                raise TypeError("The parameter %s of concat must not be BatchDataset!" % (child))

            if not self._children_flag_and_nums[index][0] and self._children_flag_and_nums[index][1]:

                tem_value = cumulative_samples_nums + self._children_flag_and_nums[index][1]

                if not self._children_flag_and_nums[index][1] >= sampler.num_shards:
                    if tem_value < sampler.num_shards:
                        self._children_start_end_index_[index][0] = cumulative_samples_nums
                        self._children_start_end_index_[index][1] = tem_value
                    else:
                        self._children_start_end_index_[index][0] = cumulative_samples_nums
                        self._children_start_end_index_[index][1] = tem_value % sampler.num_shards

                tem_sampler = copy.deepcopy(sampler)
                tem_sampler.set_offset(cumulative_samples_nums)
                child.sampler = tem_sampler

            cumulative_samples_nums += self.children_sizes_[index]
            cumulative_samples_nums %= sampler.num_shards

    def get_args(self):
        args = super().get_args()

        if self._sampler is not None:
            args["sampler"] = self._sampler
        args["children_flag_and_nums"] = self._children_flag_and_nums
        args["children_start_end_index"] = self._children_start_end_index_
        return args


class RenameDataset(DatasetOp):
    """
    The result of applying Rename operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be Renamed.
        input_columns (list[str]): List of names of the input columns.
        output_columns (list[str]): List of names of the output columns.
    """

    def __init__(self, input_dataset, input_columns, output_columns):
        super().__init__()
        if not isinstance(input_columns, list):
            input_columns = [input_columns]
        if not isinstance(output_columns, list):
            output_columns = [output_columns]
        self.input_column_names = input_columns
        self.output_column_names = output_columns
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs

    def get_args(self):
        args = super().get_args()
        args["input_columns"] = self.input_column_names
        args["output_columns"] = self.output_column_names
        return args


class ProjectDataset(DatasetOp):
    """
    The result of applying Project operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be Projected.
        columns (list[str]): List of names of the columns to project.
        prefetch_size (int, optional): Prefetch number of records ahead of the
            user's request (default=None).
    """

    def __init__(self, input_dataset, columns, prefetch_size=None):
        super().__init__()
        if not isinstance(columns, list):
            columns = [columns]
        self.columns = columns
        self.children.append(input_dataset)
        self.prefetch_size = prefetch_size

        input_dataset.parent.append(self)
        self._input_indexs = input_dataset.input_indexs

    def get_args(self):
        args = super().get_args()
        args["columns"] = self.columns
        args["prefetch_size"] = self.prefetch_size
        return args


class TransferDataset(DatasetOp):
    """
    The result of applying TDT operator to the input Dataset.

    Args:
        input_dataset (Dataset): Input Dataset to be transferred.
        queue_name (str): Name of device queue.
        device_id (int): ID of device.
        device_type (str): Type of device, including "CPU", "GPU", and "Ascend".
        send_epoch_end (bool, optional): Whether to send end of sequence to device or not (default=True).
    """

    def __init__(self, input_dataset, queue_name, device_id, device_type, send_epoch_end=True):
        super().__init__()
        self.children.append(input_dataset)
        input_dataset.parent.append(self)
        self.queue_name = queue_name
        self._input_indexs = input_dataset.input_indexs
        self._device_type = device_type
        self._device_id = device_id
        self._send_epoch_end = send_epoch_end
        self.iterator = None

    def get_args(self):
        args = super().get_args()
        args["queue_name"] = self.queue_name
        args["device_type"] = self._device_type
        args["device_id"] = self._device_id
        args["send_epoch_end"] = self._send_epoch_end
        if hasattr(self.children[0], "__total_batch__"):
            args["total_batch"] = self.children[0].__total_batch__
        return args

    def create_dict_iterator(self, num_epochs=-1, output_numpy=False):
        raise RuntimeError("TransferDataset is not iterable.")

    def create_tuple_iterator(self, columns=None, num_epochs=-1, output_numpy=False):
        raise RuntimeError("TransferDataset is not iterable.")

    def __iter__(self):
        raise RuntimeError("TransferDataset is not iterable.")

    def output_shapes(self):
        raise RuntimeError("TransferDataset does not support output_shapes.")

    def output_types(self):
        raise RuntimeError("TransferDataset does not support output_types.")

    def send(self, num_epochs=-1):
        # need to keep iterator alive so the executionTree is not destroyed
        if self._noop_mode():
            return
        if self.iterator is not None:
            del self.iterator
        self.iterator = TupleIterator(self, num_epochs=num_epochs)

    def stop_send(self):
        self.iterator.depipeline.StopSend()

    def continue_send(self):
        self.iterator.depipeline.ContinueSend()


class RangeDataset(MappableDataset):
    """
    A source dataset that reads and parses datasets stored on disk in a range.

    Args:
        start (int): Starting index.
        stop (int): Ending index.
        step (int): Step size in the range specified by start and stop.
    """

    def __init__(self, start, stop, step):
        super().__init__()
        self.start = start
        self.stop = stop
        self.step = step

    def get_args(self):
        args = super().get_args()
        args["start"] = self.start
        args["stop"] = self.stop
        args["step"] = self.step
        return args

    def is_shuffled(self):
        return False

    def is_sharded(self):
        return False

    def get_dataset_size(self):
        if self.dataset_size is None:
            self.dataset_size = math.ceil((self.stop - self.start) / self.step)
        return self.dataset_size


def _select_sampler(num_samples, input_sampler, shuffle, num_shards, shard_id, non_mappable=False):
    """
    Create sampler based on user input.

    Args:
        num_samples (int): Number of samples.
        input_sampler (Union[Iterable, Sampler]): Sampler from user.
        shuffle (bool): Shuffle.
        num_shards (int): Number of shard for sharding.
        shard_id (int): Shard ID.
        non_mappable (bool, optional): Indicate if caller is non-mappable dataset for special handling (default=False).
    """
    if non_mappable is True and all(arg is None for arg in [num_samples, shuffle, num_shards, shard_id, input_sampler]):
        return None

    if input_sampler is not None:
        # If the user provided a sampler, then it doesn't matter what the other args are because
        # we are being asked specifically to use the given sampler.
        # That means the following arguments: num_shards, shard_id, shuffle, num_samples should all
        # be None. Consider this example:
        #     sampler = ds.DistributedSampler(num_shards=8, shard_id=3, shuffle=shuffle)
        #     data1 = ds.VOCDataset(voc_dir, decode=True, sampler=sampler, num_shards=4, shard_id=1)
        # In this case, the user has given different sample-related arguments that contradict each other.
        # To prevent this, only allow the user to manually specify the sampler if those arguments are all None
        if (isinstance(input_sampler, (samplers.SequentialSampler, samplers.DistributedSampler,
                                       samplers.RandomSampler, samplers.SubsetRandomSampler,
                                       samplers.WeightedRandomSampler, samplers.Sampler)) and
                (any(arg is not None for arg in [num_shards, shard_id, shuffle, num_samples]))):
            raise ValueError(
                'Conflicting arguments during sampler assignments. num_samples: {}, num_shards: {},'
                ' shard_id: {}, shuffle: {})'.format(num_samples, num_shards, shard_id, shuffle))
        return input_sampler
    if shuffle is None:
        if num_shards is not None:
            # If shuffle is not specified, sharding enabled, use distributed random sampler
            shuffle = True
            return samplers.DistributedSampler(num_shards, shard_id, shuffle=shuffle, num_samples=num_samples)
        # If shuffle is not specified, sharding disabled, use random sampler
        if num_samples is not None:
            return samplers.RandomSampler(replacement=True, num_samples=num_samples)
        return samplers.RandomSampler(num_samples=num_samples)
    if shuffle is True:
        if num_shards is not None:
            # If shuffle enabled, sharding enabled, use distributed random sampler
            return samplers.DistributedSampler(num_shards, shard_id, shuffle=shuffle, num_samples=num_samples)
        # If shuffle enabled, sharding disabled, use random sampler
        if num_samples is not None:
            return samplers.RandomSampler(replacement=True, num_samples=num_samples)
        return samplers.RandomSampler(num_samples=num_samples)
    if num_shards is not None:
        # If shuffle disabled, sharding enabled, use distributed sequential sampler
        return samplers.DistributedSampler(num_shards, shard_id, shuffle=shuffle, num_samples=num_samples)
    # If shuffle disabled, sharding disabled, use sequential sampler
    return samplers.SequentialSampler(num_samples=num_samples)


class ImageFolderDataset(MappableDataset):
    """
    A source dataset that reads images from a tree of directories.

    All images within one folder have the same label.
    The generated dataset has two columns ['image', 'label'].
    The shape of the image column is [image_size] if decode flag is False, or [H,W,C]
    otherwise.
    The type of the image tensor is uint8. The label is a scalar int32 tensor.
    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Args:
        dataset_dir (str): Path to the root directory that contains the dataset.
        num_samples (int, optional): The number of images to be included in the dataset
            (default=None, all images).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, set in the config).
        shuffle (bool, optional): Whether or not to perform shuffle on the dataset
            (default=None, expected order behavior shown in the table).
        sampler (Sampler, optional): Object used to choose samples from the
            dataset (default=None, expected order behavior shown in the table).
        extensions (list[str], optional): List of file extensions to be
            included in the dataset (default=None).
        class_indexing (dict, optional): A str-to-int mapping from folder name to index
            (default=None, the folder names will be sorted
            alphabetically and each class will be given a
            unique index starting from 0).
        decode (bool, optional): Decode the images after reading (default=False).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Raises:
        RuntimeError: If sampler and shuffle are specified at the same time.
        RuntimeError: If sampler and sharding are specified at the same time.
        RuntimeError: If num_shards is specified but shard_id is None.
        RuntimeError: If shard_id is specified but num_shards is None.
        RuntimeError: If class_indexing is not a dictionary.
        ValueError: If shard_id is invalid (< 0 or >= num_shards).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> # Set path to the imagefolder directory.
        >>> # This directory needs to contain sub-directories which contain the images
        >>> dataset_dir = "/path/to/imagefolder_directory"
        >>>
        >>> # 1) Read all samples (image files) in dataset_dir with 8 threads
        >>> imagefolder_dataset = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8)
        >>>
        >>> # 2) Read all samples (image files) from folder cat and folder dog with label 0 and 1
        >>> imagefolder_dataset = ds.ImageFolderDataset(dataset_dir, class_indexing={"cat":0, "dog":1})
        >>>
        >>> # 3) Read all samples (image files) in dataset_dir with extensions .JPEG and .png (case sensitive)
        >>> imagefolder_dataset = ds.ImageFolderDataset(dataset_dir, extensions=[".JPEG", ".png"])
    """

    @check_imagefolderdataset
    def __init__(self, dataset_dir, num_samples=None, num_parallel_workers=None,
                 shuffle=None, sampler=None, extensions=None, class_indexing=None,
                 decode=False, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)

        self.dataset_dir = dataset_dir
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        self.shuffle_level = shuffle
        self.extensions = extensions
        self.class_indexing = class_indexing
        self.decode = decode
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_dir"] = self.dataset_dir
        args["num_samples"] = self.num_samples
        args["sampler"] = self.sampler
        args["shuffle"] = self.shuffle_level
        args["extensions"] = self.extensions
        args["class_indexing"] = self.class_indexing
        args["decode"] = self.decode
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = ImageFolderOp.get_num_rows_and_classes(self.dataset_dir)[0]
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()
            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler
        return self.dataset_size

    def num_classes(self):
        """
        Get the number of classes in dataset.

        Return:
            Number, number of classes.
        """
        return ImageFolderOp.get_num_rows_and_classes(self.dataset_dir)[1]

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class MnistDataset(MappableDataset):
    """
    A source dataset for reading and parsing the MNIST dataset.

    The generated dataset has two columns ['image', 'label'].
    The type of the image tensor is uint8. The label is a scalar uint32 tensor.
    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Citation of Mnist dataset.

    .. code-block::

        @article{lecun2010mnist,
        title        = {MNIST handwritten digit database},
        author       = {LeCun, Yann and Cortes, Corinna and Burges, CJ},
        journal      = {ATT Labs [Online]},
        volume       = {2},
        year         = {2010},
        howpublished = {http://yann.lecun.com/exdb/mnist},
        description  = {The MNIST database of handwritten digits has a training set of 60,000 examples,
                        and a test set of 10,000 examples. It is a subset of a larger set available from
                        NIST. The digits have been size-normalized and centered in a fixed-size image.}
        }

    Args:
        dataset_dir (str): Path to the root directory that contains the dataset.
        usage (str, optional): Usage of this dataset, can be "train", "test" or "all" . "train" will read from 60,000
            train samples, "test" will read from 10,000 test samples, "all" will read from all 70,000 samples.
            (default=None, all samples)
        num_samples (int, optional): The number of images to be included in the dataset
            (default=None, all images).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, set in the config).
        shuffle (bool, optional): Whether or not to perform shuffle on the dataset
            (default=None, expected order behavior shown in the table).
        sampler (Sampler, optional): Object used to choose samples from the
            dataset (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Raises:
        RuntimeError: If sampler and shuffle are specified at the same time.
        RuntimeError: If sampler and sharding are specified at the same time.
        RuntimeError: If num_shards is specified but shard_id is None.
        RuntimeError: If shard_id is specified but num_shards is None.
        ValueError: If shard_id is invalid (< 0 or >= num_shards).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_dir = "/path/to/mnist_folder"
        >>> # Read 3 samples from MNIST dataset
        >>> mnist_dataset = ds.MnistDataset(dataset_dir=dataset_dir, num_samples=3)
        >>> # Note: In mnist_dataset dataset, each dictionary has keys "image" and "label"
    """

    @check_mnist_cifar_dataset
    def __init__(self, dataset_dir, usage=None, num_samples=None, num_parallel_workers=None,
                 shuffle=None, sampler=None, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)

        self.dataset_dir = dataset_dir
        self.usage = usage
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        self.shuffle_level = shuffle
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_dir"] = self.dataset_dir
        args["usage"] = self.usage
        args["num_samples"] = self.num_samples
        args["shuffle"] = self.shuffle_level
        args["sampler"] = self.sampler
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = MnistOp.get_num_rows(self.dataset_dir, "all" if self.usage is None else self.usage)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()
            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler
        return self.dataset_size

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class MindDataset(MappableDataset):
    """
    A source dataset that reads MindRecord files.

    Args:
        dataset_file (Union[str, list[str]]): If dataset_file is a str, it represents for
            a file name of one component of a mindrecord source, other files with identical source
            in the same path will be found and loaded automatically. If dataset_file is a list,
            it represents for a list of dataset files to be read directly.
        columns_list (list[str], optional): List of columns to be read (default=None).
        num_parallel_workers (int, optional): The number of readers (default=None).
        shuffle (bool, optional): Whether or not to perform shuffle on the dataset
            (default=None, performs shuffle).
        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        sampler (Sampler, optional): Object used to choose samples from the
            dataset (default=None, sampler is exclusive
            with shuffle and block_reader). Support list: SubsetRandomSampler,
            PkSampler, RandomSampler, SequentialSampler, DistributedSampler.
        padded_sample (dict, optional): Samples will be appended to dataset, where
            keys are the same as column_list.
        num_padded (int, optional): Number of padding samples. Dataset size
            plus num_padded should be divisible by num_shards.
        num_samples (int, optional): The number of samples to be included in the dataset
            (default=None, all samples).

    Raises:
        ValueError: If num_shards is specified but shard_id is None.
        ValueError: If shard_id is specified but num_shards is None.
    """

    @check_minddataset
    def __init__(self, dataset_file, columns_list=None, num_parallel_workers=None,
                 shuffle=None, num_shards=None, shard_id=None,
                 sampler=None, padded_sample=None,
                 num_padded=None, num_samples=None):
        super().__init__(num_parallel_workers)
        if isinstance(dataset_file, list):
            self.load_dataset = False
        else:
            self.load_dataset = True
        self.dataset_file = dataset_file
        self.columns_list = columns_list
        self.shuffle_option = shuffle
        self.num_shards = num_shards
        self.shard_id = shard_id
        if shuffle is False:
            logger.warning("WARN: global shuffle is not used.")

        if sampler is not None:
            if isinstance(sampler, (samplers.SubsetRandomSampler, samplers.PKSampler,
                                    samplers.DistributedSampler, samplers.RandomSampler,
                                    samplers.SequentialSampler)) is False:
                raise ValueError("The sampler is not supported yet.")

        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        if num_padded is None:
            num_padded = 0

        self.padded_sample = padded_sample
        self.num_padded = num_padded

    def get_args(self):
        args = super().get_args()
        padded_sample = None
        if self.padded_sample:
            padded_sample = {}
            for k, v in self.padded_sample.items():
                if isinstance(v, np.ndarray):
                    padded_sample[k] = v.tobytes()
                else:
                    padded_sample[k] = v
        args["dataset_file"] = self.dataset_file
        args["load_dataset"] = self.load_dataset
        args["columns_list"] = self.columns_list
        args["shuffle_option"] = self.shuffle_option
        args["num_samples"] = self.num_samples
        args["num_padded"] = self.num_padded
        args["padded_sample"] = padded_sample
        args["sampler"] = self.sampler
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            if self.load_dataset:
                dataset_file = [self.dataset_file]
            else:
                dataset_file = self.dataset_file
            num_rows = MindRecordOp.get_num_rows(dataset_file, self.load_dataset, self.sampler, self.num_padded)
            self.dataset_size = num_rows
        return self.dataset_size

    def is_shuffled(self):
        if self.shuffle_option is None:
            return True

        return self.shuffle_option or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


def _iter_fn(dataset, num_samples):
    """
    Generator function wrapper for iterable dataset.
    """
    if num_samples is not None:
        ds_iter = iter(dataset)
        for _ in range(num_samples):
            try:
                val = next(ds_iter)
            except StopIteration:
                return
            # convert output tensors to ndarrays
            yield tuple([np.array(x, copy=False) for x in val])
    else:
        for val in dataset:
            # convert output tensors to ndarrays
            yield tuple([np.array(x, copy=False) for x in val])


def _generator_fn(generator, num_samples):
    """
    Generator function wrapper for generator function dataset.
    """
    if num_samples is not None:
        gen_iter = generator()
        for _ in range(num_samples):
            try:
                val = next(gen_iter)
            except StopIteration:
                return
            yield val
    else:
        gen_iter = generator()
        for val in gen_iter:
            yield val


def _py_sampler_fn(sampler, num_samples, dataset):
    """
    Generator function wrapper for mappable dataset with Python sampler.
    """
    if num_samples is not None:
        sampler_iter = iter(sampler)
        for _ in range(num_samples):
            try:
                idx = next(sampler_iter)
            except StopIteration:
                return
            val = dataset[idx]
            # convert output tensors to ndarrays
            yield tuple([np.array(x, copy=False) for x in val])
    else:
        for i in sampler:
            val = dataset[i]
            # convert output tensors to ndarrays
            yield tuple([np.array(x, copy=False) for x in val])


def _cpp_sampler_fn(sampler, dataset):
    """
    Generator function wrapper for mappable dataset with cpp sampler.
    """
    indices = sampler.get_indices()
    for i in indices:
        val = dataset[i]
        # convert output tensors to ndarrays
        yield tuple([np.array(x, copy=False) for x in val])


def _cpp_sampler_fn_mp(sampler, sample_fn):
    """
    Multiprocessing generator function wrapper for mappable dataset with cpp sampler.
    """
    indices = sampler.get_indices()
    return sample_fn.process(indices)


def _py_sampler_fn_mp(sampler, num_samples, sample_fn):
    """
    Multiprocessing generator function wrapper for mappable dataset with Python sampler.
    """
    indices = _fetch_py_sampler_indices(sampler, num_samples)
    return sample_fn.process(indices)


def _fetch_py_sampler_indices(sampler, num_samples):
    """
    Indice fetcher for Python sampler.
    """
    if num_samples is not None:
        sampler_iter = iter(sampler)
        ret = []
        for _ in range(num_samples):
            try:
                val = next(sampler_iter)
                ret.append(val)
            except StopIteration:
                break
        return ret
    return [i for i in sampler]


def _fill_worker_indices(workers, indices, idx):
    """
    Worker index queue filler, fill worker index queue in round robin order.
    """
    num_worker = len(workers)
    while idx < len(indices):
        try:
            workers[idx % num_worker].put(indices[idx])
            idx += 1
        except queue.Full:
            break
    return idx


class SamplerFn:
    """
    Multiprocessing or multithread generator function wrapper master process.
    """

    def __init__(self, dataset, num_worker, multi_process):
        self.workers = []
        self.num_worker = num_worker
        self.multi_process = multi_process
        # Event for end of epoch
        if multi_process is True:
            self.eof = multiprocessing.Event()
        else:
            self.eof = threading.Event()
        # Create workers
        for _ in range(num_worker):
            if multi_process is True:
                worker = _GeneratorWorkerMp(dataset, self.eof)
                worker.daemon = True
                # When multi processes fork a subprocess, the lock of the main process is copied to the subprocess,
                # which may cause deadlock. Therefore, the subprocess startup is performed in che initialization phase.
                # In this phase, the main process is not locked.
                worker.start()
            else:
                worker = _GeneratorWorkerMt(dataset, self.eof)
                worker.daemon = True
            self.workers.append(worker)

    def process(self, indices):
        """
        The main process, start the child process or child thread, and fill the index queue.
        Get the result and return.
        """
        for w in self.workers:
            # Check whether the queue of the subprocess is empty.
            if not w.queue_empty():
                raise Exception("The queue of the subprocess is not empty.")
            # Start all workers
            if not w.is_alive():
                w.start()

        # Fill initial index queues
        idx_cursor = 0
        idx_cursor = _fill_worker_indices(self.workers, indices, idx_cursor)

        # Fetch results
        for i in range(len(indices)):
            # Fetch result and put index
            try:
                result = self.workers[i % self.num_worker].get()
            except queue.Empty:
                raise Exception("Generator worker process timeout")
            except KeyboardInterrupt:
                self.eof.set()
                for w in self.workers:
                    w.terminate()
                    w.join()
                raise Exception("Generator worker receives KeyboardInterrupt")
            if idx_cursor < len(indices):
                idx_cursor = _fill_worker_indices(self.workers, indices, idx_cursor)
            yield tuple([np.array(x, copy=False) for x in result])

    def __del__(self):
        self.eof.set()


def _generator_worker_loop(dataset, idx_queue, result_queue, eof):
    """
    Multithread or multiprocess generator worker process loop.
    """
    while True:
        # Fetch index, block
        try:
            idx = idx_queue.get(timeout=1)
        except KeyboardInterrupt:
            raise Exception("Generator worker receives KeyboardInterrupt")
        except queue.Empty:
            if eof.is_set():
                return
            # If end-of-file (eof) is not set, continue to get data from idx_queue
            continue
        if idx is None:
            # When the queue is out of scope from master process, a None item can be fetched from the queue.
            # Upon receiving None, worker process should check if eof is set.
            assert eof.is_set(), ""
            return
        if eof.is_set():
            return
        # Fetch data, any exception from __getitem__ will terminate worker and timeout master process
        result = dataset[idx]
        # Send data, block
        while True:
            try:
                result_queue.put(result, timeout=5)
            except KeyboardInterrupt:
                raise Exception("Generator worker receives KeyboardInterrupt")
            except queue.Full:
                if eof.is_set():
                    return
                # If eof is not set, continue to put data to result_queue
                continue
            break
        del result, idx


class _GeneratorWorkerMt(threading.Thread):
    """
    Worker process for multithread Generator.
    """

    def __init__(self, dataset, eof):
        self.idx_queue = queue.Queue(16)
        self.res_queue = queue.Queue(16)
        super().__init__(target=_generator_worker_loop, args=(dataset, self.idx_queue, self.res_queue, eof))

    def put(self, item):
        """
        Put function for worker index queue. Never block. Raise queue.Full on failure.
        """
        self.idx_queue.put_nowait(item)

    def get(self):
        """
        Get function for worker result queue. Block with timeout.
        """
        return self.res_queue.get(timeout=30)

    def queue_empty(self):
        if not self.idx_queue.empty():
            logger.error("idx_queue is not empty")
            return False
        if not self.res_queue.empty():
            logger.error("res_queue is not empty")
            return False
        return True


class _GeneratorWorkerMp(multiprocessing.Process):
    """
    Worker process for multiprocess Generator.
    """

    def __init__(self, dataset, eof):
        self.idx_queue = multiprocessing.Queue(16)
        self.res_queue = multiprocessing.Queue(16)
        super().__init__(target=_generator_worker_loop, args=(dataset, self.idx_queue, self.res_queue, eof))

    def put(self, item):
        """
        Put function for worker index queue. Never block. Raise queue.Full on failure.
        """
        self.idx_queue.put_nowait(item)

    def get(self):
        """
        Get function for worker result queue. Block with timeout.
        """
        # Relax 10s to 30s, since it sometimes will cause "Generator worker process timeout"
        # when we run too many iterators with infinite epoch(num_epoch=-1)
        return self.res_queue.get(timeout=30)

    def queue_empty(self):
        if not self.idx_queue.empty():
            logger.error("idx_queue is not empty")
            return False
        if not self.res_queue.empty():
            logger.error("res_queue is not empty")
            return False
        return True

    def __del__(self):
        # Try to destruct here, sometimes the class itself will be destructed in advance,
        # so "self" will be a NoneType
        try:
            self.terminate()
        except AttributeError:
            pass


class GeneratorDataset(MappableDataset):
    """
    A source dataset that generates data from Python by invoking Python data source each epoch.

    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Args:
        source (Union[Callable, Iterable, Random Accessible]):
            A generator callable object, an iterable Python object or a random accessible Python object.
            Callable source is required to return a tuple of NumPy arrays as a row of the dataset on source().next().
            Iterable source is required to return a tuple of NumPy arrays as a row of the dataset on
            iter(source).next().
            Random accessible source is required to return a tuple of NumPy arrays as a row of the dataset on
            source[idx].
        column_names (list[str], optional): List of column names of the dataset (default=None). Users are required to
            provide either column_names or schema.
        column_types (list[mindspore.dtype], optional): List of column data types of the dataset (default=None).
            If provided, sanity check will be performed on generator output.
        schema (Union[Schema, str], optional): Path to the JSON schema file or schema object (default=None). Users are
            required to provide either column_names or schema. If both are provided, schema will be used.
        num_samples (int, optional): The number of samples to be included in the dataset
            (default=None, all images).
        num_parallel_workers (int, optional): Number of subprocesses used to fetch the dataset in parallel (default=1).
        shuffle (bool, optional): Whether or not to perform shuffle on the dataset. Random accessible input is required.
            (default=None, expected order behavior shown in the table).
        sampler (Union[Sampler, Iterable], optional): Object used to choose samples from the dataset. Random accessible
            input is required (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
            When this argument is specified, 'num_samples' will not used. Random accessible input is required.
        shard_id (int, optional): The shard ID within num_shards (default=None). This argument must be specified only
            when num_shards is also specified. Random accessible input is required.
        python_multiprocessing (bool, optional): Parallelize Python operations with multiple worker process. This
            option could be beneficial if the Python operation is computational heavy (default=True).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> # 1) Multidimensional generator function as callable input
        >>> def GeneratorMD():
        >>>     for i in range(64):
        >>>         yield (np.array([[i, i + 1], [i + 2, i + 3]]),)
        >>> # Create multi_dimension_generator_dataset with GeneratorMD and column name "multi_dimensional_data"
        >>> multi_dimension_generator_dataset = ds.GeneratorDataset(GeneratorMD, ["multi_dimensional_data"])
        >>>
        >>> # 2) Multi-column generator function as callable input
        >>> def GeneratorMC(maxid = 64):
        >>>     for i in range(maxid):
        >>>         yield (np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]]))
        >>> # Create multi_column_generator_dataset with GeneratorMC and column names "col1" and "col2"
        >>> multi_column_generator_dataset = ds.GeneratorDataset(GeneratorMC, ["col1", "col2"])
        >>>
        >>> # 3) Iterable dataset as iterable input
        >>> class MyIterable():
        >>>     def __iter__(self):
        >>>         return # User implementation
        >>> # Create iterable_generator_dataset with MyIterable object
        >>> iterable_generator_dataset = ds.GeneratorDataset(MyIterable(), ["col1"])
        >>>
        >>> # 4) Random accessible dataset as random accessible input
        >>> class MyRA():
        >>>     def __getitem__(self, index):
        >>>         return # User implementation
        >>> # Create ra_generator_dataset with MyRA object
        >>> ra_generator_dataset = ds.GeneratorDataset(MyRA(), ["col1"])
        >>> # List/Dict/Tuple is also random accessible
        >>> list_generator = ds.GeneratorDataset([(np.array(0),), (np.array(1)), (np.array(2))], ["col1"])
        >>>
        >>> # 5) Built-in Sampler
        >>> my_generator = ds.GeneratorDataset(my_ds, ["img", "label"], sampler=samplers.RandomSampler())
    """

    @check_generatordataset
    def __init__(self, source, column_names=None, column_types=None, schema=None, num_samples=None,
                 num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None,
                 python_multiprocessing=True):
        super().__init__(num_parallel_workers)
        self.source = source
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        self.num_shards = num_shards
        self.python_multiprocessing = python_multiprocessing

        if column_names is not None and not isinstance(column_names, list):
            column_names = [column_names]
        self.column_names = column_names

        if column_types is not None:
            self.column_types = mstypelist_to_detypelist(column_types)
        else:
            self.column_types = column_types

        if schema is not None:
            self.schema = schema
            if not isinstance(schema, Schema):
                self.schema = Schema(schema)
            self.column_names = []
            self.column_types = []
            for col in self.schema.columns:
                self.column_names.append(col["name"])
                self.column_types.append(DataType(col["type"]))

    def get_args(self):
        args = super().get_args()
        args["source"] = self.source
        args["column_names"] = self.column_names
        args["column_types"] = self.column_types
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            if hasattr(self.source, "__len__"):
                if not self.num_shards:
                    self.dataset_size = len(self.source)
                else:
                    self.dataset_size = math.ceil(len(self.source) / self.num_shards)

                rows_from_sampler = self._get_sampler_dataset_size()
                if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                    self.dataset_size = rows_from_sampler
            else:
                num_rows = 0
                for _ in self.create_dict_iterator(num_epochs=1, output_numpy=True):
                    num_rows += 1
                self.dataset_size = num_rows
        return self.dataset_size

    def __deepcopy__(self, memodict):
        if id(self) in memodict:
            return memodict[id(self)]
        cls = self.__class__
        new_op = cls.__new__(cls)
        memodict[id(self)] = new_op
        new_op.children = copy.deepcopy(self.children, memodict)
        new_op.parent = copy.deepcopy(self.parent, memodict)
        new_op.num_parallel_workers = copy.deepcopy(self.num_parallel_workers, memodict)
        new_op.column_types = copy.deepcopy(self.column_types, memodict)
        new_op.column_names = copy.deepcopy(self.column_names, memodict)
        new_op.num_samples = copy.deepcopy(self.num_samples, memodict)
        new_op.dataset_size = self.dataset_size
        new_op.sampler = copy.deepcopy(self.sampler)
        if new_op.sampler is not None and hasattr(self.source, "__getitem__"):
            if isinstance(new_op.sampler, (samplers.SequentialSampler, samplers.DistributedSampler,
                                           samplers.RandomSampler, samplers.SubsetRandomSampler,
                                           samplers.WeightedRandomSampler, samplers.Sampler)):
                sampler_instance = new_op.sampler.create()
                sampler_instance.set_num_rows(len(self.source))
                sampler_instance.initialize()
                if new_op.num_parallel_workers > 1:
                    sample_fn = SamplerFn(self.source, new_op.num_parallel_workers, self.python_multiprocessing)
                    new_op.source = (lambda: _cpp_sampler_fn_mp(sampler_instance, sample_fn))
                else:
                    new_op.source = (lambda: _cpp_sampler_fn(sampler_instance, self.source))
            else:
                if new_op.num_parallel_workers > 1:
                    sample_fn = SamplerFn(self.source, new_op.num_parallel_workers, self.python_multiprocessing)
                    new_op.source = (lambda: _py_sampler_fn_mp(new_op.sampler, new_op.num_samples, sample_fn))
                else:
                    new_op.source = (lambda: _py_sampler_fn(new_op.sampler, new_op.num_samples, self.source))
        else:
            try:
                iter(self.source)
            except TypeError:
                # Use generator function if input callable
                new_op.source = (lambda: _generator_fn(self.source, new_op.num_samples))
            else:
                # Use iterator function if input is iterable
                # Random accessible input is also iterable
                new_op.source = (lambda: _iter_fn(self.source, new_op.num_samples))

        return new_op

    def is_shuffled(self):
        return self.sampler.is_shuffled()

    def is_sharded(self):
        return self.sampler.is_sharded()


class TFRecordDataset(SourceDataset):
    """
    A source dataset that reads and parses datasets stored on disk in TFData format.

    Args:
        dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
            pattern of files. The list will be sorted in a lexicographical order.
        schema (Union[str, Schema], optional): Path to the JSON schema file or schema object (default=None).
            If the schema is not provided, the meta data from the TFData file is considered the schema.
        columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
        num_samples (int, optional): Number of samples (rows) to read (default=None).
            If num_samples is None and numRows(parsed from schema) does not exist, read the full dataset;
            If num_samples is None and numRows(parsed from schema) is greater than 0, read numRows rows;
            If both num_samples and numRows(parsed from schema) are greater than 0, read num_samples rows.
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
            (default=Shuffle.GLOBAL).
            If shuffle is False, no shuffling will be performed;
            If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
            Otherwise, there are two levels of shuffling:

            - Shuffle.GLOBAL: Shuffle both the files and samples.

            - Shuffle.FILES: Shuffle files only.

        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        shard_equal_rows (bool, optional): Get equal rows for all shards(default=False). If shard_equal_rows
            is false, number of rows of each shard may be not equal.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.
    Examples:
        >>> import mindspore.dataset as ds
        >>> import mindspore.common.dtype as mstype
        >>>
        >>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple tf data files
        >>>
        >>> # 1) Get all rows from dataset_files with no explicit schema
        >>> # The meta-data in the first row will be used as a schema.
        >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files)
        >>>
        >>> # 2) Get all rows from dataset_files with user-defined schema
        >>> schema = ds.Schema()
        >>> schema.add_column('col_1d', de_type=mindspore.int64, shape=[2])
        >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema=schema)
        >>>
        >>> # 3) Get all rows from dataset_files with schema file "./schema.json"
        >>> tfdataset = ds.TFRecordDataset(dataset_files=dataset_files, schema="./schema.json")
    """

    @check_tfrecorddataset
    def __init__(self, dataset_files, schema=None, columns_list=None, num_samples=None, num_parallel_workers=None,
                 shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, shard_equal_rows=False, cache=None):
        super().__init__(num_parallel_workers)
        self.dataset_files = self._find_files(dataset_files)
        self.dataset_files.sort()
        self.num_shards = num_shards
        self.shard_id = shard_id
        schema_obj = None
        if (schema is not None) and (not isinstance(schema, Schema)):
            schema_obj = Schema(schema)  # read the schema file and convert to schema object to validate it
        self.schema = schema
        self.columns_list = columns_list
        self.num_samples = num_samples
        self.cache = cache
        if schema_obj is not None and num_samples is None:
            self.num_samples = schema_obj.num_rows

        if not isinstance(shuffle, (bool, Shuffle)):
            raise TypeError("shuffle must be of type boolean or enum 'Shuffle'.")
        if not isinstance(shuffle, Shuffle):
            if shuffle:
                self.shuffle_level = Shuffle.GLOBAL
                self.shuffle_files = True
            else:
                self.shuffle_level = None
                self.shuffle_files = False
        else:
            self.shuffle_level = shuffle
            self.shuffle_files = True

        # The TF record dataset does not directly support a sampler.  It has provided sampling arguments
        # (shuffle, num_samples, num_shards, shard_id) and it DOES support sampling if somewhere above it in
        # the pipeline contains a cache.  If there is no cache above it, then this sampler is not used.
        sampler_shuffle = self.shuffle_files
        sampler = None
        self.sampler = _select_sampler(self.num_samples, sampler, sampler_shuffle, num_shards, shard_id,
                                       non_mappable=True)
        self.shard_equal_rows = shard_equal_rows

    def get_args(self):
        args = super().get_args()
        args["dataset_files"] = self.dataset_files
        if self.schema is not None:
            if isinstance(self.schema, Schema):
                self.schema.datasetType = 'TF'
                if self.num_samples is not None:
                    self.schema.num_rows = self.num_samples
                args["schema_json_string"] = self.schema.to_json()
            else:
                args["schema_file_path"] = self.schema
        args["schema"] = self.schema
        args["columns_list"] = self.columns_list
        args["num_samples"] = self.num_samples
        if self.shuffle_files is not None:
            args["shuffle_files"] = self.shuffle_files
        args["shuffle_global"] = (self.shuffle_level == Shuffle.GLOBAL)
        args["shuffle"] = self.shuffle_level
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["shard_equal_rows"] = self.shard_equal_rows
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        args["sampler"] = self.sampler
        return args

    def get_dataset_size(self, estimate=False):
        """
        Get the number of batches in an epoch.

        Note:
            Because the TFRecord format does not save metadata, all files need to be traversed to obtain
            the total amount of data. Therefore, this api is slow.

        Args:
            estimate (bool, optional): Fast estimation of the dataset size instead of a full scan.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = TFReaderOp.get_num_rows(self.dataset_files, 8, estimate)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            if self.num_samples is not None and self.num_samples < self.dataset_size:
                self.dataset_size = self.num_samples
        return self.dataset_size

    def is_shuffled(self):
        return self.shuffle_files

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return False


class ManifestDataset(MappableDataset):
    """
    A source dataset that reads images from a manifest file.

    The generated dataset has two columns ['image', 'label'].
    The shape of the image column is [image_size] if decode flag is False, or [H,W,C]
    otherwise.
    The type of the image tensor is uint8. The label is a scalar uint64 tensor.
    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Args:
        dataset_file (str): File to be read.
        usage (str, optional): acceptable usages include train, eval and inference (default="train").
        num_samples (int, optional): The number of images to be included in the dataset.
            (default=None, all images).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (bool, optional): Whether to perform shuffle on the dataset (default=None, expected
            order behavior shown in the table).
        sampler (Sampler, optional): Object used to choose samples from the
            dataset (default=None, expected order behavior shown in the table).
        class_indexing (dict, optional): A str-to-int mapping from label name to index
            (default=None, the folder names will be sorted alphabetically and each
            class will be given a unique index starting from 0).
        decode (bool, optional): decode the images after reading (default=False).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Raises:
        RuntimeError: If sampler and shuffle are specified at the same time.
        RuntimeError: If sampler and sharding are specified at the same time.
        RuntimeError: If num_shards is specified but shard_id is None.
        RuntimeError: If shard_id is specified but num_shards is None.
        RuntimeError: If class_indexing is not a dictionary.
        ValueError: If shard_id is invalid (< 0 or >= num_shards).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_file = "/path/to/manifest_file.manifest"
        >>>
        >>> # 1) Read all samples specified in manifest_file dataset with 8 threads for training
        >>> manifest_dataset = ds.ManifestDataset(dataset_file, usage="train", num_parallel_workers=8)
        >>>
        >>> # 2) Read samples (specified in manifest_file.manifest) for shard 0
        >>> # in a 2-way distributed training setup
        >>> manifest_dataset = ds.ManifestDataset(dataset_file, num_shards=2, shard_id=0)

    """

    @check_manifestdataset
    def __init__(self, dataset_file, usage="train", num_samples=None, num_parallel_workers=None,
                 shuffle=None, sampler=None, class_indexing=None, decode=False, num_shards=None, shard_id=None,
                 cache=None):
        super().__init__(num_parallel_workers)

        self.dataset_file = dataset_file
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)

        if class_indexing is not None and not isinstance(class_indexing, dict):
            raise RuntimeError("class_indexing must be a dictionary.")

        self.num_samples = num_samples
        self.class_indexing = class_indexing
        self.decode = decode
        self.usage = usage
        self.shuffle_level = shuffle
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_file"] = self.dataset_file
        args["usage"] = self.usage
        args["num_samples"] = self.num_samples
        args["shuffle"] = self.shuffle_level
        args["sampler"] = self.sampler
        args["class_indexing"] = self.class_indexing
        args["decode"] = self.decode
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            if self.class_indexing is None:
                class_indexing = dict()
            else:
                class_indexing = self.class_indexing

            num_rows = ManifestOp.get_num_rows_and_classes(self.dataset_file, class_indexing, self.usage)[0]
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()

            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler
        return self.dataset_size

    def num_classes(self):
        """
        Get the number of classes in a dataset.

        Return:
            Number, number of classes.
        """
        if self.class_indexing is None:
            class_indexing = dict()
        else:
            class_indexing = self.class_indexing

        return ManifestOp.get_num_rows_and_classes(self.dataset_file, class_indexing, self.usage)[1]

    def get_class_indexing(self):
        """
        Get the class index.

        Return:
            Dict, A str-to-int mapping from label name to index.
        """
        if self.class_indexing is None:
            class_indexing = dict()
        else:
            class_indexing = self.class_indexing

        return ManifestOp.get_class_indexing(self.dataset_file, class_indexing, self.usage)

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class Cifar10Dataset(MappableDataset):
    """
    A source dataset that reads cifar10 data.

    The generated dataset has two columns ['image', 'label'].
    The type of the image tensor is uint8. The label is a scalar uint32 tensor.
    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Citation of Cifar10 dataset.

    .. code-block::

        @techreport{Krizhevsky09,
        author       = {Alex Krizhevsky},
        title        = {Learning multiple layers of features from tiny images},
        institution  = {},
        year         = {2009},
        howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html},
        description  = {The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
                        with 6000 images per class. There are 50000 training images and 10000 test images.}
        }

    Args:
        dataset_dir (str): Path to the root directory that contains the dataset.
        usage (str, optional): Usage of this dataset, can be "train", "test" or "all" . "train" will read from 50,000
            train samples, "test" will read from 10,000 test samples, "all" will read from all 60,000 samples.
            (default=None, all samples)
        num_samples (int, optional): The number of images to be included in the dataset.
            (default=None, all images).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (bool, optional): Whether to perform shuffle on the dataset (default=None, expected
            order behavior shown in the table).
        sampler (Sampler, optional): Object used to choose samples from the
            dataset (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Raises:
        RuntimeError: If sampler and shuffle are specified at the same time.
        RuntimeError: If sampler and sharding are specified at the same time.
        RuntimeError: If num_shards is specified but shard_id is None.
        RuntimeError: If shard_id is specified but num_shards is None.
        ValueError: If shard_id is invalid (< 0 or >= num_shards).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_dir = "/path/to/cifar10_dataset_directory"
        >>>
        >>> # 1) Get all samples from CIFAR10 dataset in sequence
        >>> dataset = ds.Cifar10Dataset(dataset_dir=dataset_dir, shuffle=False)
        >>>
        >>> # 2) Randomly select 350 samples from CIFAR10 dataset
        >>> dataset = ds.Cifar10Dataset(dataset_dir=dataset_dir, num_samples=350, shuffle=True)
        >>>
        >>> # 3) Get samples from CIFAR10 dataset for shard 0 in a 2-way distributed training
        >>> dataset = ds.Cifar10Dataset(dataset_dir=dataset_dir, num_shards=2, shard_id=0)
        >>>
        >>> # In CIFAR10 dataset, each dictionary has keys "image" and "label"
    """

    @check_mnist_cifar_dataset
    def __init__(self, dataset_dir, usage=None, num_samples=None, num_parallel_workers=None,
                 shuffle=None, sampler=None, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)

        self.dataset_dir = dataset_dir
        self.usage = usage
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.shuffle_level = shuffle
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_dir"] = self.dataset_dir
        args["usage"] = self.usage
        args["num_samples"] = self.num_samples
        args["sampler"] = self.sampler
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["shuffle"] = self.shuffle_level
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = CifarOp.get_num_rows(self.dataset_dir, "all" if self.usage is None else self.usage, True)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()

            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler

        return self.dataset_size

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class Cifar100Dataset(MappableDataset):
    """
    A source dataset that reads cifar100 data.

    The generated dataset has three columns ['image', 'coarse_label', 'fine_label'].
    The type of the image tensor is uint8. The coarse and fine labels are each a scalar uint32 tensor.
    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Citation of Cifar100 dataset.

    .. code-block::

        @techreport{Krizhevsky09,
        author       = {Alex Krizhevsky},
        title        = {Learning multiple layers of features from tiny images},
        institution  = {},
        year         = {2009},
        howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html},
        description  = {This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images
                        each. There are 500 training images and 100 testing images per class. The 100 classes in
                        the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the
                        class to which it belongs) and a "coarse" label (the superclass to which it belongs).}
        }

    Args:
        dataset_dir (str): Path to the root directory that contains the dataset.
        usage (str, optional): Usage of this dataset, can be "train", "test" or "all" . "train" will read from 50,000
            train samples, "test" will read from 10,000 test samples, "all" will read from all 60,000 samples.
            (default=None, all samples)
        num_samples (int, optional): The number of images to be included in the dataset.
            (default=None, all images).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (bool, optional): Whether to perform shuffle on the dataset (default=None, expected
            order behavior shown in the table).
        sampler (Sampler, optional): Object used to choose samples from the
            dataset (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Raises:
        RuntimeError: If sampler and shuffle are specified at the same time.
        RuntimeError: If sampler and sharding are specified at the same time.
        RuntimeError: If num_shards is specified but shard_id is None.
        RuntimeError: If shard_id is specified but num_shards is None.
        ValueError: If shard_id is invalid (< 0 or >= num_shards).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_dir = "/path/to/cifar100_dataset_directory"
        >>>
        >>> # 1) Get all samples from CIFAR100 dataset in sequence
        >>> cifar100_dataset = ds.Cifar100Dataset(dataset_dir=dataset_dir, shuffle=False)
        >>>
        >>> # 2) Randomly select 350 samples from CIFAR100 dataset
        >>> cifar100_dataset = ds.Cifar100Dataset(dataset_dir=dataset_dir, num_samples=350, shuffle=True)
        >>>
        >>> # In CIFAR100 dataset, each dictionary has 3 keys: "image", "fine_label" and "coarse_label"
    """

    @check_mnist_cifar_dataset
    def __init__(self, dataset_dir, usage=None, num_samples=None, num_parallel_workers=None,
                 shuffle=None, sampler=None, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)

        self.dataset_dir = dataset_dir
        self.usage = usage
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.shuffle_level = shuffle
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_dir"] = self.dataset_dir
        args["usage"] = self.usage
        args["num_samples"] = self.num_samples
        args["sampler"] = self.sampler
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["shuffle"] = self.shuffle_level
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = CifarOp.get_num_rows(self.dataset_dir, "all" if self.usage is None else self.usage, False)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()

            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler

        return self.dataset_size

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class RandomDataset(SourceDataset):
    """
    A source dataset that generates random data.

    Args:
        total_rows (int): Number of rows for the dataset to generate (default=None, number of rows is random)
        schema (Union[str, Schema], optional): Path to the JSON schema file or schema object (default=None).
            If the schema is not provided, the random dataset generates a random schema.
        columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
        num_samples (int): number of samples to draw from the total. (default=None, which means all rows)
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.
        shuffle (bool, optional): Whether or not to perform shuffle on the dataset
            (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
    """

    @check_random_dataset
    def __init__(self, total_rows=None, schema=None, columns_list=None, num_samples=None, num_parallel_workers=None,
                 cache=None, shuffle=None, num_shards=None, shard_id=None):
        super().__init__(num_parallel_workers)
        schema_obj = None
        if (schema is not None) and (not isinstance(schema, Schema)):
            schema_obj = Schema(schema)  # read the schema file and convert to schema object to validate it
        self.schema = schema
        self.columns_list = columns_list
        sampler = None
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id, non_mappable=True)
        self.num_samples = num_samples
        self.cache = cache
        if schema_obj is not None and total_rows is None:
            self.total_rows = schema_obj.num_rows
        elif total_rows is None:
            self.total_rows = 0
        else:
            self.total_rows = total_rows
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.shuffle_level = shuffle

    def get_args(self):
        args = super().get_args()
        if self.schema is not None:
            if isinstance(self.schema, Schema):
                self.schema.datasetType = 'Random'
                if self.total_rows is not None:
                    self.schema.num_rows = self.total_rows
                args["schema_json_string"] = self.schema.to_json()
            else:
                args["schema_file_path"] = self.schema
        args["schema"] = self.schema
        args["columns_list"] = self.columns_list
        args["num_samples"] = self.num_samples
        args["total_rows"] = self.total_rows
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        args["sampler"] = self.sampler
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = CifarOp.get_num_rows(self.dataset_dir, True)

            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()

        if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
            self.dataset_size = rows_from_sampler

        return self.dataset_size

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class Schema:
    """
    Class to represent a schema of a dataset.

    Args:
        schema_file(str): Path of schema file (default=None).

    Return:
        Schema object, schema info about dataset.

    Raises:
        RuntimeError: If schema file failed to load.

    Example:
        >>> import mindspore.dataset as ds
        >>> import mindspore.common.dtype as mstype
        >>>
        >>> # Create schema; specify column name, mindspore.dtype and shape of the column
        >>> schema = ds.Schema()
        >>> schema.add_column('col1', de_type=mindspore.int64, shape=[2])
    """

    def __init__(self, schema_file=None):
        self.num_rows = None
        if schema_file is None:
            self.columns = []
            self.dataset_type = ''
        else:
            if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK):
                raise ValueError("The file %s does not exist or permission denied!" % schema_file)
            try:
                with open(schema_file, 'r') as load_f:
                    json_obj = json.load(load_f)
            except json.decoder.JSONDecodeError:
                raise RuntimeError("Schema file failed to load.")
            except UnicodeDecodeError:
                raise RuntimeError("Schema file failed to decode.")
            except Exception:
                raise RuntimeError("Schema file failed to open.")
            self.from_json(json_obj)

    @check_add_column
    def add_column(self, name, de_type, shape=None):
        """
        Add new column to the schema.

        Args:
            name (str): Name of the column.
            de_type (str): Data type of the column.
            shape (list[int], optional): Shape of the column
                (default=None, [-1] which is an unknown shape of rank 1).

        Raises:
            ValueError: If column type is unknown.
        """
        new_column = dict()
        new_column["name"] = name
        if isinstance(de_type, typing.Type):
            de_type = mstype_to_detype(de_type)
            new_column["type"] = str(de_type)
        else:
            new_column["type"] = str(DataType(de_type))

        if shape is not None:
            new_column["shape"] = shape
            new_column["rank"] = len(shape)
        else:
            new_column["rank"] = 1
        self.columns.append(new_column)

    def to_json(self):
        """
        Get a JSON string of the schema.

        Returns:
            Str, JSON string of the schema.
        """
        json_file = dict()
        json_file["columns"] = self.columns
        if self.dataset_type:
            json_file["datasetType"] = self.dataset_type
        if self.num_rows:
            json_file["numRows"] = self.num_rows
        return json.dumps(json_file, indent=2)

    def parse_columns(self, columns):
        """
        Parse the columns and add it to self.

        Args:
            columns (Union[dict, list[dict]]): Dataset attribute information, decoded from schema file.

                - list[dict], 'name' and 'type' must be in keys, 'shape' optional.

                - dict, columns.keys() as name, columns.values() is dict, and 'type' inside, 'shape' optional.

        Raises:
            RuntimeError: If failed to parse columns.
            RuntimeError: If unknown items in columns.
            RuntimeError: If column's name field is missing.
            RuntimeError: If column's type field is missing.

        Example:
            >>> schema = Schema()
            >>> columns1 = [{'name': 'image', 'type': 'int8', 'shape': [3, 3]},
            >>>             {'name': 'label', 'type': 'int8', 'shape': [1]}]
            >>> schema.parse_columns(columns1)
            >>> columns2 = {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}}
            >>> schema.parse_columns(columns2)
        """
        self.columns = []
        if isinstance(columns, list):
            for column in columns:
                try:
                    name = column.pop("name")
                except KeyError:
                    raise RuntimeError("Column's name is missing")
                try:
                    de_type = column.pop("type")
                except KeyError:
                    raise RuntimeError("Column' type is missing")
                shape = column.pop("shape", None)
                column.pop("t_impl", None)
                column.pop("rank", None)
                if column:
                    raise RuntimeError("Unknown field {}".format(",".join(column.keys())))
                self.add_column(name, de_type, shape)
        elif isinstance(columns, dict):
            for key, value in columns.items():
                name = key
                try:
                    de_type = value.pop("type")
                except KeyError:
                    raise RuntimeError("Column' type is missing")
                shape = value.pop("shape", None)
                value.pop("t_impl", None)
                value.pop("rank", None)
                if value:
                    raise RuntimeError("Unknown field {}".format(",".join(value.keys())))
                self.add_column(name, de_type, shape)
        else:
            raise RuntimeError("columns must be dict or list, columns contain name, type, shape(optional).")

    def from_json(self, json_obj):
        """
        Get schema file from JSON file.

        Args:
            json_obj(dictionary): Object of JSON parsed.

        Raises:
            RuntimeError: if there is unknown item in the object.
            RuntimeError: if dataset type is missing in the object.
            RuntimeError: if columns are missing in the object.
        """
        if not isinstance(json_obj, dict) or json_obj is None:
            raise ValueError("Expected non-empty dict.")
        for k, v in json_obj.items():
            if k == "datasetType":
                self.dataset_type = v
            elif k == "numRows":
                self.num_rows = v
            elif k == "columns":
                self.parse_columns(v)
            else:
                raise RuntimeError("Unknown field %s" % k)

        if self.columns is None:
            raise RuntimeError("Columns are missing.")
        if self.num_rows is not None:
            if not isinstance(self.num_rows, int) or self.num_rows <= 0:
                raise ValueError("numRows must be greater than 0")

    def __str__(self):
        return self.to_json()


class VOCDataset(MappableDataset):
    """
    A source dataset for reading and parsing VOC dataset.

    The generated dataset has multiple columns :

        - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['label', dtype=uint32],
          ['difficult', dtype=uint32], ['truncate', dtype=uint32]].
        - task='Segmentation', column: [['image', dtype=uint8], ['target',dtype=uint8]].

    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Citation of VOC dataset.

    .. code-block::

        @article{Everingham10,
        author       = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
        title        = {The Pascal Visual Object Classes (VOC) Challenge},
        journal      = {International Journal of Computer Vision},
        volume       = {88},
        year         = {2010},
        number       = {2},
        month        = {jun},
        pages        = {303--338},
        biburl       = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex},
        howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc{year}/index.html},
        description  = {The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual
                        object category recognition and detection, providing the vision and machine
                        learning communities with a standard dataset of images and annotation, and
                        standard evaluation procedures.}
        }

    Args:
        dataset_dir (str): Path to the root directory that contains the dataset.
        task (str): Set the task type of reading voc data, now only support "Segmentation" or "Detection"
            (default="Segmentation").
        usage (str): The type of data list text file to be read (default="train").
        class_indexing (dict, optional): A str-to-int mapping from label name to index, only valid in
            "Detection" task (default=None, the folder names will be sorted alphabetically and each
            class will be given a unique index starting from 0).
        num_samples (int, optional): The number of images to be included in the dataset
            (default=None, all images).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (bool, optional): Whether to perform shuffle on the dataset (default=None, expected
            order behavior shown in the table).
        decode (bool, optional): Decode the images after reading (default=False).
        sampler (Sampler, optional): Object used to choose samples from the dataset
            (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Raises:
        RuntimeError: If xml of Annotations is an invalid format.
        RuntimeError: If xml of Annotations loss attribution of "object".
        RuntimeError: If xml of Annotations loss attribution of "bndbox".
        RuntimeError: If sampler and shuffle are specified at the same time.
        RuntimeError: If sampler and sharding are specified at the same time.
        RuntimeError: If num_shards is specified but shard_id is None.
        RuntimeError: If shard_id is specified but num_shards is None.
        ValueError: If task is not equal 'Segmentation' or 'Detection'.
        ValueError: If task equal 'Segmentation' but class_indexing is not None.
        ValueError: If txt related to mode is not exist.
        ValueError: If shard_id is invalid (< 0 or >= num_shards).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_dir = "/path/to/voc_dataset_directory"
        >>>
        >>> # 1) Read VOC data for segmentatation training
        >>> voc_dataset = ds.VOCDataset(dataset_dir, task="Segmentation", usage="train")
        >>>
        >>> # 2) Read VOC data for detection training
        >>> voc_dataset = ds.VOCDataset(dataset_dir, task="Detection", usage="train")
        >>>
        >>> # 3) Read all VOC dataset samples in dataset_dir with 8 threads in random order
        >>> voc_dataset = ds.VOCDataset(dataset_dir, task="Detection", usage="train", num_parallel_workers=8)
        >>>
        >>> # 4) Read then decode all VOC dataset samples in dataset_dir in sequence
        >>> voc_dataset = ds.VOCDataset(dataset_dir, task="Detection", usage="train", decode=True, shuffle=False)
        >>>
        >>> # In VOC dataset, if task='Segmentation', each dictionary has keys "image" and "target"
        >>> # In VOC dataset, if task='Detection', each dictionary has keys "image" and "annotation"
    """

    @check_vocdataset
    def __init__(self, dataset_dir, task="Segmentation", usage="train", class_indexing=None, num_samples=None,
                 num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None,
                 cache=None):
        super().__init__(num_parallel_workers)
        self.dataset_dir = dataset_dir
        self.task = task
        self.usage = usage
        self.class_indexing = class_indexing
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        self.decode = decode
        self.shuffle_level = shuffle
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_dir"] = self.dataset_dir
        args["task"] = self.task
        args["usage"] = self.usage
        args["class_indexing"] = self.class_indexing
        args["num_samples"] = self.num_samples
        args["sampler"] = self.sampler
        args["decode"] = self.decode
        args["shuffle"] = self.shuffle_level
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            if self.num_samples is None:
                num_samples = 0
            else:
                num_samples = self.num_samples

            if self.class_indexing is None:
                class_indexing = dict()
            else:
                class_indexing = self.class_indexing

            num_rows = VOCOp.get_num_rows(self.dataset_dir, self.task, self.usage, class_indexing, num_samples)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()

            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler

        return self.dataset_size

    def get_class_indexing(self):
        """
        Get the class index.

        Return:
            Dict, A str-to-int mapping from label name to index.
        """
        if self.task != "Detection":
            raise NotImplementedError()

        if self.class_indexing is None:
            class_indexing = dict()
        else:
            class_indexing = self.class_indexing

        return VOCOp.get_class_indexing(self.dataset_dir, self.task, self.usage, class_indexing)

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class CocoDataset(MappableDataset):
    """
    A source dataset for reading and parsing COCO dataset.

    CocoDataset support four kinds of task: 2017 Train/Val/Test Detection, Keypoints, Stuff, Panoptic.

    The generated dataset has multi-columns :

        - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
          ['iscrowd', dtype=uint32]].
        - task='Stuff', column: [['image', dtype=uint8], ['segmentation',dtype=float32], ['iscrowd',dtype=uint32]].
        - task='Keypoint', column: [['image', dtype=uint8], ['keypoints', dtype=float32],
          ['num_keypoints', dtype=uint32]].
        - task='Panoptic', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
          ['iscrowd', dtype=uint32], ['area', dtype=uint32]].

    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. CocoDataset doesn't support
    PKSampler. The table below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Citation of Coco dataset.

    .. code-block::

        @article{DBLP:journals/corr/LinMBHPRDZ14,
        author        = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and
                         Lubomir D. Bourdev and  Ross B. Girshick and James Hays and
                         Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick},
        title         = {Microsoft {COCO:} Common Objects in Context},
        journal       = {CoRR},
        volume        = {abs/1405.0312},
        year          = {2014},
        url           = {http://arxiv.org/abs/1405.0312},
        archivePrefix = {arXiv},
        eprint        = {1405.0312},
        timestamp     = {Mon, 13 Aug 2018 16:48:13 +0200},
        biburl        = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
        bibsource     = {dblp computer science bibliography, https://dblp.org},
        description   = {COCO is a large-scale object detection, segmentation, and captioning dataset.
                         It contains 91 common object categories with 82 of them having more than 5,000
                         labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer
                         categories but more instances per category.}
        }

    Args:
        dataset_dir (str): Path to the root directory that contains the dataset.
        annotation_file (str): Path to the annotation JSON.
        task (str): Set the task type for reading COCO data.  Supported task types:
            'Detection', 'Stuff', 'Panoptic' and 'Keypoint' (default='Detection').
        num_samples (int, optional): The number of images to be included in the dataset
            (default=None, all images).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the configuration file).
        shuffle (bool, optional): Whether to perform shuffle on the dataset (default=None, expected
            order behavior shown in the table).
        decode (bool, optional): Decode the images after reading (default=False).
        sampler (Sampler, optional): Object used to choose samples from the dataset
            (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Raises:
        RuntimeError: If sampler and shuffle are specified at the same time.
        RuntimeError: If sampler and sharding are specified at the same time.
        RuntimeError: If num_shards is specified but shard_id is None.
        RuntimeError: If shard_id is specified but num_shards is None.
        RuntimeError: If parse JSON file failed.
        ValueError: If task is not in ['Detection', 'Stuff', 'Panoptic', 'Keypoint'].
        ValueError: If annotation_file is not exist.
        ValueError: If dataset_dir is not exist.
        ValueError: If shard_id is invalid (< 0 or >= num_shards).

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_dir = "/path/to/coco_dataset_directory/image_folder"
        >>> annotation_file = "/path/to/coco_dataset_directory/annotation_folder/annotation.json"
        >>>
        >>> # 1) Read COCO data for Detection task
        >>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Detection')
        >>>
        >>> # 2) Read COCO data for Stuff task
        >>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Stuff')
        >>>
        >>> # 3) Read COCO data for Panoptic task
        >>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Panoptic')
        >>>
        >>> # 4) Read COCO data for Keypoint task
        >>> coco_dataset = ds.CocoDataset(dataset_dir, annotation_file=annotation_file, task='Keypoint')
        >>>
        >>> # In COCO dataset, each dictionary has keys "image" and "annotation"
    """

    @check_cocodataset
    def __init__(self, dataset_dir, annotation_file, task="Detection", num_samples=None, num_parallel_workers=None,
                 shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)
        self.dataset_dir = dataset_dir
        self.annotation_file = annotation_file
        self.task = task
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_samples = num_samples
        self.decode = decode
        self.shuffle_level = shuffle
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_dir"] = self.dataset_dir
        args["annotation_file"] = self.annotation_file
        args["task"] = self.task
        args["num_samples"] = self.num_samples
        args["sampler"] = self.sampler
        args["decode"] = self.decode
        args["shuffle"] = self.shuffle_level
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = CocoOp.get_num_rows(self.dataset_dir, self.annotation_file, self.task)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            rows_from_sampler = self._get_sampler_dataset_size()

            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler

        return self.dataset_size

    def get_class_indexing(self):
        """
        Get the class index.

        Return:
            Dict, A str-to-int mapping from label name to index.
        """
        if self.task not in {"Detection", "Panoptic"}:
            raise NotImplementedError("Only 'Detection' and 'Panoptic' support get_class_indexing.")

        class_index = CocoOp.get_class_indexing(self.dataset_dir, self.annotation_file, self.task)
        return dict(class_index)

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class CelebADataset(MappableDataset):
    """
    A source dataset for reading and parsing CelebA dataset. Currently supported: list_attr_celeba.txt only.

    Note:
        The generated dataset has two columns ['image', 'attr'].
        The type of the image tensor is uint8. The attribute tensor is uint32 and one hot type.

    Citation of CelebA dataset.

    .. code-block::

        @article{DBLP:journals/corr/LiuLWT14,
        author    = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
        title     = {Deep Learning Face Attributes in the Wild},
        journal   = {CoRR},
        volume    = {abs/1411.7766},
        year      = {2014},
        url       = {http://arxiv.org/abs/1411.7766},
        archivePrefix = {arXiv},
        eprint    = {1411.7766},
        timestamp = {Tue, 10 Dec 2019 15:37:26 +0100},
        biburl    = {https://dblp.org/rec/journals/corr/LiuLWT14.bib},
        bibsource = {dblp computer science bibliography, https://dblp.org},
        howpublished = {http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html},
        description  = {CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset
                        with more than 200K celebrity images, each with 40 attribute annotations.
                        The images in this dataset cover large pose variations and background clutter.
                        CelebA has large diversities, large quantities, and rich annotations, including
                        * 10,177 number of identities,
                        * 202,599 number of face images, and
                        * 5 landmark locations, 40 binary attributes annotations per image.
                        The dataset can be employed as the training and test sets for the following computer
                        vision tasks: face attribute recognition, face detection, landmark (or facial part)
                        localization, and face editing & synthesis.}
        }

    Args:
        dataset_dir (str): Path to the root directory that contains the dataset.
        num_parallel_workers (int, optional): Number of workers to read the data (default=value set in the config).
        shuffle (bool, optional): Whether to perform shuffle on the dataset (default=None).
        usage (str): one of 'all', 'train', 'valid' or 'test'.
        sampler (Sampler, optional): Object used to choose samples from the dataset (default=None).
        decode (bool, optional): decode the images after reading (default=False).
        extensions (list[str], optional): List of file extensions to be
            included in the dataset (default=None).
        num_samples (int, optional): The number of images to be included in the dataset.
            (default=None, all images).
        num_shards (int, optional): Number of shards that the dataset will be divided
            into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_dir = "/path/to/celeba_directory"
        >>> dataset = ds.CelebADataset(dataset_dir=dataset_dir, usage='train')
    """

    @check_celebadataset
    def __init__(self, dataset_dir, num_parallel_workers=None, shuffle=None, usage='all', sampler=None, decode=False,
                 extensions=None, num_samples=None, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)
        self.dataset_dir = dataset_dir
        self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
        self.num_parallel_workers = num_parallel_workers
        self.decode = decode
        self.extensions = extensions
        self.num_samples = num_samples
        self.usage = usage
        self.num_shards = num_shards
        self.shard_id = shard_id
        self.shuffle_level = shuffle
        self.cache = cache

        if usage != "all":
            dir = os.path.realpath(self.dataset_dir)
            partition_file = os.path.join(dir, "list_eval_partition.txt")
            if os.path.exists(partition_file) is False:
                raise RuntimeError("Partition file can not be found when usage is not 'all'.")

    def get_args(self):
        args = super().get_args()
        args["dataset_dir"] = self.dataset_dir
        args["sampler"] = self.sampler
        args["shuffle"] = self.shuffle_level
        args["decode"] = self.decode
        args["extensions"] = self.extensions
        args["num_samples"] = self.num_samples
        args["usage"] = self.usage
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            dir = os.path.realpath(self.dataset_dir)
            attr_file = os.path.join(dir, "list_attr_celeba.txt")
            num_rows = ''
            try:
                with open(attr_file, 'r') as f:
                    num_rows = int(f.readline())
            except FileNotFoundError:
                raise RuntimeError("attr file can not be found.")
            except BaseException:
                raise RuntimeError("Get dataset size failed from attribution file.")
            if self.usage != 'all':
                partition_file = os.path.join(dir, "list_eval_partition.txt")
                usage_type = 0
                partition_num = 0
                if self.usage == "train":
                    usage_type = 0
                elif self.usage == "valid":
                    usage_type = 1
                elif self.usage == "test":
                    usage_type = 2
                try:
                    with open(partition_file, 'r') as f:
                        for line in f.readlines():
                            split_line = line.split(' ')
                            if int(split_line[1]) == usage_type:
                                partition_num += 1
                except FileNotFoundError:
                    raise RuntimeError("Partition file can not be found")
                if partition_num < num_rows:
                    num_rows = partition_num

            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            if self.num_samples is not None and self.num_samples < self.dataset_size:
                self.dataset_size = self.num_samples
            rows_from_sampler = self._get_sampler_dataset_size()
            if rows_from_sampler is not None and rows_from_sampler < self.dataset_size:
                self.dataset_size = rows_from_sampler
        return self.dataset_size

    def is_shuffled(self):
        if self.shuffle_level is None:
            return True

        return self.shuffle_level or self.sampler.is_shuffled()

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return self.sampler.is_sharded()


class CLUEDataset(SourceDataset):
    """
    A source dataset that reads and parses CLUE datasets.
    CLUE, the Chinese Language Understanding Evaluation Benchmark, is a collection of datasets, baselines,
    pre-trained models, corpus and leaderboard. Supported CLUE classification tasks: 'AFQMC', 'TNEWS', 'IFLYTEK',
    'CMNLI', 'WSC' and 'CSL'.

    Citation of CLUE dataset.

    .. code-block::

        @article{CLUEbenchmark,
        title   = {CLUE: A Chinese Language Understanding Evaluation Benchmark},
        author  = {Liang Xu, Xuanwei Zhang, Lu Li, Hai Hu, Chenjie Cao, Weitang Liu, Junyi Li, Yudong Li,
                   Kai Sun, Yechen Xu, Yiming Cui, Cong Yu, Qianqian Dong, Yin Tian, Dian Yu, Bo Shi, Jun Zeng,
                   Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou,
                   Shaoweihua Liu, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Zhenzhong Lan},
        journal = {arXiv preprint arXiv:2004.05986},
        year    = {2020},
        howpublished = {https://github.com/CLUEbenchmark/CLUE},
        description  = {CLUE, a Chinese Language Understanding Evaluation benchmark. It contains eight different
                        tasks, including single-sentence classification, sentence pair classification, and machine
                        reading comprehension.}
        }

    Args:
        dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for
            a pattern of files. The list will be sorted in a lexicographical order.
        task (str, optional): The kind of task, one of 'AFQMC', 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' and 'CSL'.
            (default=AFQMC).
        usage (str, optional): Need train, test or eval data (default="train").
        num_samples (int, optional): Number of samples (rows) to read (default=None, reads the full dataset).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
            (default=Shuffle.GLOBAL).
            If shuffle is False, no shuffling will be performed;
            If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
            Otherwise, there are two levels of shuffling:

            - Shuffle.GLOBAL: Shuffle both the files and samples.

            - Shuffle.FILES: Shuffle files only.

        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
        >>> dataset = ds.CLUEDataset(dataset_files=dataset_files, task='AFQMC', usage='train')
    """

    @check_cluedataset
    def __init__(self, dataset_files, task='AFQMC', usage='train', num_samples=None,
                 num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)
        self.dataset_files = self._find_files(dataset_files)
        self.dataset_files.sort()
        self.num_samples = num_samples
        self.task_dict = {
            'AFQMC': {
                'train': {
                    'sentence1': 'sentence1',
                    'sentence2': 'sentence2',
                    'label': 'label'
                },
                'test': {
                    'id': 'id',
                    'sentence1': 'sentence1',
                    'sentence2': 'sentence2'
                },
                'eval': {
                    'sentence1': 'sentence1',
                    'sentence2': 'sentence2',
                    'label': 'label'
                }
            },
            'CMNLI': {
                'train': {
                    'sentence1': 'sentence1',
                    'sentence2': 'sentence2',
                    'label': 'label'
                },
                'test': {
                    'id': 'id',
                    'sentence1': 'sentence1',
                    'sentence2': 'sentence2'
                },
                'eval': {
                    'sentence1': 'sentence1',
                    'sentence2': 'sentence2',
                    'label': 'label'
                }
            },
            'CSL': {
                'train': {
                    'id': 'id',
                    'abst': 'abst',
                    'keyword': 'keyword',
                    'label': 'label'
                },
                'test': {
                    'id': 'id',
                    'abst': 'abst',
                    'keyword': 'keyword'
                },
                'eval': {
                    'id': 'id',
                    'abst': 'abst',
                    'keyword': 'keyword',
                    'label': 'label'
                }
            },
            'IFLYTEK': {
                'train': {
                    'label': 'label',
                    'label_des': 'label_des',
                    'sentence': 'sentence'
                },
                'test': {
                    'id': 'id',
                    'sentence': 'sentence',
                },
                'eval': {
                    'label': 'label',
                    'label_des': 'label_des',
                    'sentence': 'sentence'
                }
            },
            'TNEWS': {
                'train': {
                    'label': 'label',
                    'label_desc': 'label_desc',
                    'sentence': 'sentence',
                    'keywords': 'keywords'
                },
                'test': {
                    'id': 'id',
                    'sentence': 'sentence',
                    'keywords': 'keywords'
                },
                'eval': {
                    'label': 'label',
                    'label_desc': 'label_desc',
                    'sentence': 'sentence',
                    'keywords': 'keywords'
                }
            },
            'WSC': {
                'train': {
                    'span1_index': 'target/span1_index',
                    'span2_index': 'target/span2_index',
                    'span1_text': 'target/span1_text',
                    'span2_text': 'target/span2_text',
                    'idx': 'idx',
                    'label': 'label',
                    'text': 'text'
                },
                'test': {
                    'span1_index': 'target/span1_index',
                    'span2_index': 'target/span2_index',
                    'span1_text': 'target/span1_text',
                    'span2_text': 'target/span2_text',
                    'idx': 'idx',
                    'text': 'text'
                },
                'eval': {
                    'span1_index': 'target/span1_index',
                    'span2_index': 'target/span2_index',
                    'span1_text': 'target/span1_text',
                    'span2_text': 'target/span2_text',
                    'idx': 'idx',
                    'label': 'label',
                    'text': 'text'
                }
            }
        }
        self.cols_to_keyword = self.task_dict[task][usage]

        if not isinstance(shuffle, (bool, Shuffle)):
            raise TypeError("shuffle must be of type boolean or enum 'Shuffle'.")
        if not isinstance(shuffle, Shuffle):
            if shuffle:
                self.shuffle_level = Shuffle.GLOBAL
                self.shuffle_files = True
            else:
                self.shuffle_level = None
                self.shuffle_files = False
        else:
            self.shuffle_level = shuffle
            self.shuffle_files = True

        self.num_shards = num_shards
        self.shard_id = shard_id

        # The clue dataset does not directly support a sampler.  It has provided sampling arguments
        # (shuffle, num_samples, num_shards, shard_id) and it DOES support sampling if somewhere above it in
        # the pipeline contains a cache.  If there is no cache above it, then this sampler is not used.
        sampler_shuffle = self.shuffle_files
        sampler = None
        self.sampler = _select_sampler(self.num_samples, sampler, sampler_shuffle, num_shards, shard_id,
                                       non_mappable=True)
        self.cache = cache

    def get_args(self):
        args = super().get_args()
        args["dataset_files"] = self.dataset_files
        args["num_samples"] = self.num_samples
        if self.shuffle_files is not None:
            args["shuffle_files"] = self.shuffle_files
        args["shuffle_global"] = (self.shuffle_level == Shuffle.GLOBAL)
        args["shuffle"] = self.shuffle_level
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["cols_to_keyword"] = self.cols_to_keyword
        args["sampler"] = self.sampler
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = ClueOp.get_num_rows(self.dataset_files)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            if self.num_samples is not None and self.num_samples < self.dataset_size:
                self.dataset_size = self.num_samples
        return self.dataset_size

    def is_shuffled(self):
        return self.shuffle_files

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return False


class CSVDataset(SourceDataset):
    """
    A source dataset that reads and parses comma-separated values (CSV) datasets.

    Args:
        dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search
            for a pattern of files. The list will be sorted in a lexicographical order.
        field_delim (str, optional): A string that indicates the char delimiter to separate fields (default=',').
        column_defaults (list, optional): List of default values for the CSV field (default=None). Each item
            in the list is either a valid type (float, int, or string). If this is not provided, treats all
            columns as string type.
        column_names (list[str], optional): List of column names of the dataset (default=None). If this
            is not provided, infers the column_names from the first row of CSV file.
        num_samples (int, optional): Number of samples (rows) to read (default=None, reads the full dataset).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
            (default=Shuffle.GLOBAL).
            If shuffle is False, no shuffling will be performed;
            If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
            Otherwise, there are two levels of shuffling:

            - Shuffle.GLOBAL: Shuffle both the files and samples.

            - Shuffle.FILES: Shuffle files only.

        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.


    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
        >>> dataset = ds.CSVDataset(dataset_files=dataset_files, column_names=['col1', 'col2', 'col3', 'col4'])
    """

    @check_csvdataset
    def __init__(self, dataset_files, field_delim=',', column_defaults=None, column_names=None, num_samples=None,
                 num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)
        self.dataset_files = self._find_files(dataset_files)
        self.dataset_files.sort()
        self.field_delim = field_delim
        self.column_defaults = column_defaults
        self.column_names = column_names
        self.num_samples = num_samples

        if not isinstance(shuffle, (bool, Shuffle)):
            raise TypeError("shuffle must be of type boolean or enum 'Shuffle'.")
        if not isinstance(shuffle, Shuffle):
            if shuffle:
                self.shuffle_level = Shuffle.GLOBAL
                self.shuffle_files = True
            else:
                self.shuffle_level = None
                self.shuffle_files = False
        else:
            self.shuffle_level = shuffle
            self.shuffle_files = True

        self.num_shards = num_shards
        self.shard_id = shard_id

        self.cache = cache
        # The CSV dataset does not directly support a sampler.  It has provided sampling arguments
        # (shuffle, num_samples, num_shards, shard_id) and it DOES support sampling if somewhere above it in
        # the pipeline contains a cache.  If there is no cache above it, then this sampler is not used.
        sampler_shuffle = self.shuffle_files
        sampler = None
        self.sampler = _select_sampler(self.num_samples, sampler, sampler_shuffle, num_shards, shard_id,
                                       non_mappable=True)

    def get_args(self):
        args = super().get_args()
        args["dataset_files"] = self.dataset_files
        args['field_delim'] = self.field_delim
        args['column_defaults'] = self.column_defaults
        args['column_names'] = self.column_names
        args["num_samples"] = self.num_samples
        if self.shuffle_files is not None:
            args["shuffle_files"] = self.shuffle_files
        args["shuffle_global"] = (self.shuffle_level == Shuffle.GLOBAL)
        args["shuffle"] = self.shuffle_level
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["sampler"] = self.sampler
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = CsvOp.get_num_rows(self.dataset_files, self.column_names is None)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            if self.num_samples is not None and self.num_samples < self.dataset_size:
                self.dataset_size = num_rows
        return self.dataset_size

    def is_shuffled(self):
        return self.shuffle_files

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return False


class TextFileDataset(SourceDataset):
    """
    A source dataset that reads and parses datasets stored on disk in text format.
    The generated dataset has one column ['text'].

    Args:
        dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
            pattern of files. The list will be sorted in a lexicographical order.
        num_samples (int, optional): Number of samples (rows) to read (default=None, reads the full dataset).
        num_parallel_workers (int, optional): Number of workers to read the data
            (default=None, number set in the config).
        shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
            (default=Shuffle.GLOBAL).
            If shuffle is False, no shuffling will be performed;
            If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
            Otherwise, there are two levels of shuffling:

            - Shuffle.GLOBAL: Shuffle both the files and samples.

            - Shuffle.FILES: Shuffle files only.

        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
        shard_id (int, optional): The shard ID within num_shards (default=None). This
            argument can only be specified when num_shards is also specified.
        cache (DatasetCache, optional): Tensor cache to use. (default=None which means no cache is used).
            The cache feature is under development and is not recommended.

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
        >>> dataset = ds.TextFileDataset(dataset_files=dataset_files)
    """

    @check_textfiledataset
    def __init__(self, dataset_files, num_samples=None, num_parallel_workers=None,
                 shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None):
        super().__init__(num_parallel_workers)
        self.dataset_files = self._find_files(dataset_files)
        self.dataset_files.sort()
        self.num_samples = num_samples

        if not isinstance(shuffle, (bool, Shuffle)):
            raise TypeError("shuffle must be of type boolean or enum 'Shuffle'.")
        if not isinstance(shuffle, Shuffle):
            if shuffle:
                self.shuffle_level = Shuffle.GLOBAL
                self.shuffle_files = True
            else:
                self.shuffle_level = None
                self.shuffle_files = False
        else:
            self.shuffle_level = shuffle
            self.shuffle_files = True

        self.num_shards = num_shards
        self.shard_id = shard_id

        self.cache = cache
        # The text file dataset does not directly support a sampler.  It has provided sampling arguments
        # (shuffle, num_samples, num_shards, shard_id) and it DOES support sampling if somewhere above it in
        # the pipeline contains a cache.  If there is no cache above it, then this sampler is not used.
        sampler_shuffle = self.shuffle_files
        sampler = None
        self.sampler = _select_sampler(self.num_samples, sampler, sampler_shuffle, num_shards, shard_id,
                                       non_mappable=True)

    def get_args(self):
        args = super().get_args()
        args["dataset_files"] = self.dataset_files
        args["num_samples"] = self.num_samples
        if self.shuffle_files is not None:
            args["shuffle_files"] = self.shuffle_files
        args["shuffle_global"] = (self.shuffle_level == Shuffle.GLOBAL)
        args["shuffle"] = self.shuffle_level
        args["num_shards"] = self.num_shards
        args["shard_id"] = self.shard_id
        args["sampler"] = self.sampler
        args["cache"] = self.cache.cache_client if self.cache is not None else None
        return args

    def get_dataset_size(self):
        """
        Get the number of batches in an epoch.

        Return:
            Number, number of batches.
        """
        if self.dataset_size is None:
            num_rows = TextFileOp.get_num_rows(self.dataset_files)
            self.dataset_size = get_num_rows(num_rows, self.num_shards)
            # If the user gave a num samples in the dataset, then the sampler will limit the rows returned
            # to that amount.  Account for that here in the row count
            if self.num_samples is not None and self.num_samples > 0 and num_rows > self.num_samples:
                self.dataset_size = self.num_samples
        return self.dataset_size

    def is_shuffled(self):
        return self.shuffle_files

    def is_sharded(self):
        if self.num_shards is not None:
            return self.num_shards > 1

        return False


class _NumpySlicesDataset:
    """
    Mainly for dealing with several kinds of formats of Python data, and return one row each time.
    """

    def __init__(self, data, column_list=None):
        self.column_list = None
        # Convert dict data into tuple
        if isinstance(data, dict):
            data = self.process_dict(data)

        if isinstance(data, tuple):
            self.data = ()
            data_len = len(data)
            for i in range(data_len):
                self.data = self.data + (np.array(data[i]),)
        else:
            self.data = (np.array(data),)

        # check whether the data length in each column is equal
        data_len = [len(data_item) for data_item in self.data]
        if data_len[1:] != data_len[:-1]:
            raise ValueError("Data length in each column is not equal.")

        # Init column_name
        if column_list is not None:
            self.column_list = column_list
        elif self.column_list is None:
            self.column_list = []
            column_num = len(self.data)
            for i in range(column_num):
                self.column_list.append("column_" + str(i))

    def __getitem__(self, index):
        data_row = [d[index, ...] for d in self.data]
        data_res = tuple(data_row)
        return data_res

    def __len__(self):
        return len(self.data[0])

    def process_dict(self, input_data):
        """
        Convert the dict like data into tuple format, when input is a tuple of dicts then compose it into a dict first.
        """
        # Convert pandas like dict(has "values" column) into General dict
        data_keys = list(input_data.keys())
        data_col = input_data[data_keys[0]]
        if hasattr(data_col, "values"):
            new_dict = {}
            for key in data_keys:
                item1 = input_data.pop(key)
                new_dict[key] = item1.values
            input_data = new_dict

        # Convert the data in dict into tuple
        data = ()
        keys = list(input_data.keys())
        self.column_list = keys
        for key in keys:
            value = input_data[key]
            data = data + (list(value),)

        return data


class NumpySlicesDataset(GeneratorDataset):
    """
    Create a dataset with given data slices, mainly for loading Python data into dataset.

    This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive. The table
    below shows what input arguments are allowed and their expected behavior.

    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
       :widths: 25 25 50
       :header-rows: 1

       * - Parameter 'sampler'
         - Parameter 'shuffle'
         - Expected Order Behavior
       * - None
         - None
         - random order
       * - None
         - True
         - random order
       * - None
         - False
         - sequential order
       * - Sampler object
         - None
         - order defined by sampler
       * - Sampler object
         - True
         - not allowed
       * - Sampler object
         - False
         - not allowed

    Args:
        data (Union[list, tuple, dict]) Input of given data. Supported data types include: list, tuple, dict and other
            NumPy formats. Input data will be sliced along the first dimension and generate additional rows.
            Large data is not recommended to be loaded in this way as data is loading into memory.
        column_names (list[str], optional): List of column names of the dataset (default=None). If column_names is not
            provided, when data is dict, column_names will be its keys, otherwise it will be like column_1, column_2 ...
        num_samples (int, optional): The number of samples to be included in the dataset (default=None, all images).
        num_parallel_workers (int, optional): Number of subprocesses used to fetch the dataset in parallel (default=1).
        shuffle (bool, optional): Whether or not to perform shuffle on the dataset. Random accessible input is required.
            (default=None, expected order behavior shown in the table).
        sampler (Union[Sampler, Iterable], optional): Object used to choose samples from the dataset. Random accessible
            input is required (default=None, expected order behavior shown in the table).
        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
            When this argument is specified, 'num_samples' will not used. Random accessible input is required.
        shard_id (int, optional): The shard ID within num_shards (default=None). This argument must be specified only
            when num_shards is also specified. Random accessible input is required.

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> # 1) Input data can be a list
        >>> data = [1, 2, 3]
        >>> dataset1 = ds.NumpySlicesDataset(data, column_names=["column_1"])
        >>>
        >>> # 2) Input data can be a dictionary, and column_names will be its keys
        >>> data = {"a": [1, 2], "b": [3, 4]}
        >>> dataset2 = ds.NumpySlicesDataset(data)
        >>>
        >>> # 3) Input data can be a tuple of lists (or NumPy arrays), each tuple element refers to data in each column
        >>> data = ([1, 2], [3, 4], [5, 6])
        >>> dataset3 = ds.NumpySlicesDataset(data, column_names=["column_1", "column_2", "column_3"])
        >>>
        >>> # 4) Load data from CSV file
        >>> import pandas as pd
        >>> df = pd.read_csv("file.csv")
        >>> dataset4 = ds.NumpySlicesDataset(dict(df), shuffle=False)
    """

    @check_numpyslicesdataset
    def __init__(self, data, column_names=None, num_samples=None, num_parallel_workers=1, shuffle=None,
                 sampler=None, num_shards=None, shard_id=None):
        dataset = _NumpySlicesDataset(data, column_names)
        super().__init__(dataset, column_names=dataset.column_list, num_samples=num_samples,
                         num_parallel_workers=num_parallel_workers, shuffle=shuffle, sampler=sampler,
                         num_shards=num_shards, shard_id=shard_id)


class _PaddedDataset:
    """
    Mainly for combining false samples provided by users into a dataset.

    Args:
        padded_samples (list(dict)): Data provided by user to be added to the initial Dataset.
    """

    def __init__(self, padded_samples):
        self.column_names = list(padded_samples[0].keys())
        self.padded_samples = padded_samples

    def __getitem__(self, item):
        return (self.padded_samples[item][key] for key in self.column_names)

    def __len__(self):
        return len(self.padded_samples)


class PaddedDataset(GeneratorDataset):
    """
    Create a dataset with fake data provided by user. Mainly used to add to the original data set
    and assign it to the corresponding shard.

    Args:
        padded_samples (list(dict)): Samples provided by user.

    Raises:
        TypeError: If padded_samples is not an instance of list.
        TypeError: If the element of padded_samples is not an instance of dict.
        ValueError: If the padded_samples is empty.

    Examples:
        >>> import mindspore.dataset as ds
        >>> data1 = [{'image': np.zeros(1, np.uint8)}, {'image': np.zeros(2, np.uint8)}]
        >>> ds1 = ds.PaddedDataset(data1)
    """

    @check_paddeddataset
    def __init__(self, padded_samples):
        dataset = _PaddedDataset(padded_samples)
        super().__init__(dataset, column_names=dataset.column_names,
                         num_shards=None,
                         shard_id=None, shuffle=False)
        self._dataset_size = len(dataset.padded_samples)
        self.padded_samples = padded_samples

    def get_dataset_size(self):
        return self._dataset_size


class BuildVocabDataset(DatasetOp):
    """
    Build a vocab from a dataset. This will collect all the unique words in a dataset and return a vocab
    which contains top_k most frequent words (if top_k is specified).
    This function is not meant to be called directly by user. To build vocab, use the function
    text.Vocab.from_dataset()

    Args:
        vocab (Vocab): text.vocab object.
        columns (Union[str, list], optional): Column names to get words from. It can be a list of column names
            (Default=None, all columns are used, return error if any column is not a string).
        freq_range (tuple, optional): Tuple of integers (min_frequency, max_frequency). Words within the frequency
            range will be kept. 0 <= min_frequency <= max_frequency <= total_words. min_frequency/max_frequency
            can be None, which corresponds to 0/total_words separately (default=None, all words are included).
        top_k (int, optional): top_k > 0. Number of words to be built into vocab. top_k most frequent words are
            taken. The top_k is taken after freq_range. If not enough top_k words, all words will be taken
            (default=None, all words are included).
        special_tokens (list, optional):  List of strings, each one is a special token, for example
            special_tokens=["<pad>","<unk>"] (default=None, no special tokens will be added).
        special_first (bool, optional): Whether special_tokens will be prepended/appended to vocab, If special_tokens
            is specified and special_first is set to None, special_tokens will be prepended. (default=None).
        prefetch_size (int, optional): Prefetch number of records ahead of the user's request (default=None).
    """

    def __init__(self, input_dataset, vocab, columns, freq_range, top_k, special_tokens, special_first,
                 prefetch_size=None):
        super().__init__()
        self.columns = columns
        self.children.append(input_dataset)
        self.prefetch_size = prefetch_size
        self.vocab = vocab
        self.freq_range = freq_range
        self.top_k = top_k
        self.special_tokens = special_tokens
        self.special_first = special_first
        input_dataset.parent.append(self)

    def get_args(self):
        args = super().get_args()
        args["columns"] = self.columns
        args["vocab"] = self.vocab
        args["freq_range"] = self.freq_range
        args["prefetch_size"] = self.prefetch_size
        args["top_k"] = self.top_k
        args["special_tokens"] = self.special_tokens
        args["special_first"] = self.special_first
        return args

    def __deepcopy__(self, memodict):
        if id(self) in memodict:
            return memodict[id(self)]
        cls = self.__class__
        new_op = cls.__new__(cls)
        memodict[id(self)] = new_op
        new_op.children = copy.deepcopy(self.children, memodict)
        new_op.columns = copy.deepcopy(self.columns, memodict)
        new_op.num_parallel_workers = copy.deepcopy(self.num_parallel_workers, memodict)
        new_op.prefetch_size = copy.deepcopy(self.prefetch_size, memodict)
        new_op.parent = copy.deepcopy(self.parent, memodict)
        new_op.freq_range = copy.deepcopy(self.freq_range, memodict)
        new_op.top_k = copy.deepcopy(self.top_k, memodict)
        new_op.vocab = self.vocab
        new_op.special_tokens = copy.deepcopy(self.special_tokens)
        new_op.special_first = copy.deepcopy(self.special_first)
        new_op.dataset_size = self.dataset_size

        return new_op


class BuildSentencePieceVocabDataset(DatasetOp):
    """
    Build a SentencePieceVocab from a dataset.
    This function is not meant to be called directly by user. To build vocab, use the function
    text.SentencePieceVocab.from_dataset()

    Args:
        vocab (SentencePieceVocab): text.SentencePieceVocab object.
        col_names (list): List of column names.
        vocab_size (int): Vocabulary size. The type is uint32.
        character_coverage (float): Percentage of characters covered by the model. Good defaults are:
            0.9995 for languages with rich character sets like Japanese or Chinese character sets,
            and 1.0 for other languages with small character sets.
        model_type (SentencePieceModel): Model type. Choose from unigram (default), bpe, char, or word.
            The input sentence must be pretokenized when using word type.
        params (dict): A dictionary with no incoming parameters.
    """

    def __init__(self, input_dataset, vocab, col_names, vocab_size, character_coverage, model_type, params):
        super().__init__()
        self.vocab = vocab
        self.col_names = col_names
        self.vocab_size = vocab_size
        self.children.append(input_dataset)
        self.character_coverage = character_coverage
        self.model_type = DE_C_INTER_SENTENCEPIECE_MODE[model_type]
        self.params = params
        input_dataset.parent.append(self)

    def get_args(self):
        args = super().get_args()
        args["vocab"] = self.vocab
        args["col_names"] = self.col_names
        args["vocab_size"] = self.vocab_size
        args["character_coverage"] = self.character_coverage
        args["model_type"] = self.model_type
        args["params"] = self.params
        return args

    def __deepcopy__(self, memodict):
        if id(self) in memodict:
            return memodict[id(self)]
        cls = self.__class__
        new_op = cls.__new__(cls)
        memodict[id(self)] = new_op
        new_op.children = copy.deepcopy(self.children, memodict)
        new_op.col_names = copy.deepcopy(self.col_names, memodict)
        new_op.num_parallel_workers = copy.deepcopy(self.num_parallel_workers, memodict)
        new_op.vocab_size = copy.deepcopy(self.vocab_size, memodict)
        new_op.parent = copy.deepcopy(self.parent, memodict)
        new_op.character_coverage = copy.deepcopy(self.character_coverage, memodict)
        new_op.params = copy.deepcopy(self.params, memodict)
        new_op.vocab = self.vocab
        new_op.model_type = copy.deepcopy(self.model_type)
        new_op.dataset_size = self.dataset_size
        return new_op